Systems and methods for replacing signal artifacts in a glucose sensor data stream

ABSTRACT

Systems and methods for minimizing or eliminating transient non-glucose related signal noise due to non-glucose rate limiting phenomenon such as ischemia, pH changes, temperatures changes, and the like. The system monitors a data stream from a glucose sensor and detects signal artifacts that have higher amplitude than electronic or diffusion-related system noise. The system replaces some or the entire data stream continually or intermittently including signal estimation methods that particularly address transient signal artifacts. The system is also capable of detecting the severity of the signal artifacts and selectively applying one or more signal estimation algorithm factors responsive to the severity of the signal artifacts, which includes selectively applying distinct sets of parameters to a signal estimation algorithm or selectively applying distinct signal estimation algorithms.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 15/197,349, filed Jun. 29, 2016, which is a continuation of U.S.application Ser. No. 13/181,341, filed Jul. 12, 2011, now U.S. Pat. No.9,427,183, which is a continuation of U.S. application Ser. No.10/648,849 filed Aug. 22, 2003, now U.S. Pat. No. 8,010,174. Each of theaforementioned applications is incorporated by reference herein in itsentirety, and each is hereby expressly made a part of thisspecification.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods forprocessing data received from a glucose sensor. Particularly, thepresent invention relates to systems and methods for detecting andreplacing transient non-glucose related signal artifacts, includingdetecting, estimating, predicting and otherwise minimizing the effectsof signal artifacts in a glucose sensor data stream.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which causes an arrayof physiological derangements (kidney failure, skin ulcers, or bleedinginto the vitreous of the eye) associated with the deterioration of smallblood vessels. A hypoglycemic reaction (low blood sugar) is induced byan inadvertent overdose of insulin, or after a normal dose of insulin orglucose-lowering agent accompanied by extraordinary exercise orinsufficient food intake.

Conventionally, a diabetic person carries a self-monitoring bloodglucose (SMBG) monitor, which typically comprises uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a diabeticwill normally only measure his or her glucose level two to four timesper day. Unfortunately, these time intervals are so far spread apartthat the diabetic will likely find out too late, sometimes incurringdangerous side effects, of a hyperglycemic or hypoglycemic condition. Infact, it is not only unlikely that a diabetic will take a timely SMBGvalue, but additionally the diabetic will not know if their bloodglucose value is going up (higher) or down (lower) based on conventionalmethods.

Consequently, a variety of transdermal and implantable electrochemicalsensors are being developed for continuous detecting and/or quantifyingblood glucose values. Many implantable glucose sensors suffer fromcomplications within the body and provide only short-term andless-than-accurate sensing of blood glucose. Similarly, transdermalsensors have run into problems in accurately sensing and reporting backglucose values continuously over extended periods of time. Some effortshave been made to obtain blood glucose data from implantable devices andretrospectively determine blood glucose trends for analysis, howeverthese efforts do not aid the diabetic in determining real-time bloodglucose information. Some efforts have also been made to obtain bloodglucose data from transdermal devices for prospective data analysis,however similar problems have occurred.

Data streams from glucose sensors are known to have some amount ofnoise, caused by unwanted electronic and/or diffusion-related systemnoise that degrades the quality of the data stream. Some attempts havebeen made in conventional glucose sensors to smooth the raw output datastream representative of the concentration of blood glucose in thesample, for example by smoothing or filtering of Gaussian, white,random, and/or other relatively low amplitude noise in order to improvethe signal to noise ratio, and thus data output.

SUMMARY OF THE INVENTION

Systems and methods are provided that accurately detect and replacesignal noise that is caused by substantially non-glucose reactionrate-limiting phenomena, such as ischemia, pH changes, temperaturechanges, pressure, and stress, for example, which are referred to hereinas signal artifacts. Detecting and replacing signal artifacts in a rawglucose data can provide accurate estimated glucose measurements to adiabetic patient so that they can proactively care for their conditionto safely avoid hyperglycemic and hypoglycemic conditions.

In a first embodiment a method is provided for analyzing data from aglucose sensor, including: monitoring a data stream from the sensor;detecting transient non-glucose related signal artifacts in the datastream that have a higher amplitude than a system noise; and replacingat least some of the signal artifacts using estimated glucose signalvalues.

In an aspect of the first embodiment, the data signal obtaining stepincludes receiving data from one of non-invasive, minimally invasive,and invasive glucose sensor.

In an aspect of the first embodiment, the data signal obtaining stepincludes receiving data from one of an enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,iontophoretic, and radiometric glucose sensor.

In an aspect of the first embodiment, the data signal obtaining stepincludes receiving data from a wholly implantable glucose sensor.

In an aspect of the first embodiment, the signal artifacts detectionstep includes testing for ischemia within or proximal to the glucosesensor.

In an aspect of the first embodiment, the ischemia testing step includesobtaining oxygen concentration using an oxygen sensor proximal to orwithin the glucose sensor.

In an aspect of the first embodiment, the ischemia testing step includescomparing a measurement from an oxygen sensor proximal to or within theglucose sensor with a measurement from an oxygen sensor distal from theglucose sensor.

In an aspect of the first embodiment, the glucose sensor includes anelectrochemical cell including a working electrode and a referenceelectrode, and wherein the ischemia-testing step includes pulsedamperometric detection.

In an aspect of the first embodiment, the glucose sensor includes anelectrochemical cell including working, counter and referenceelectrodes, and wherein the ischemia-testing step includes monitoringthe counter electrode.

In an aspect of the first embodiment, the glucose sensor includes anelectrochemical cell including working, counter and referenceelectrodes, and wherein the ischemia-testing step includes monitoringthe reference electrode.

In an aspect of the first embodiment, the glucose sensor includes anelectrochemical cell including an anode and a cathode, and wherein theischemia-testing step includes monitoring the cathode.

In an aspect of the first embodiment, the signal artifacts detectionstep includes monitoring a level of pH proximal to the sensor.

In an aspect of the first embodiment, the signal artifacts detectionstep includes monitoring a temperature proximal to the sensor.

In an aspect of the first embodiment, the signal artifacts detectionstep includes comparing a level of pH proximal to and distal to thesensor.

In an aspect of the first embodiment, the signal artifacts detectionstep includes comparing a temperature proximal to and distal to thesensor.

In an aspect of the first embodiment, the signal artifacts detectionstep includes monitoring a pressure or stress within the glucose sensor.

In an aspect of the first embodiment, the signal artifacts detectionstep includes evaluating historical data for high amplitude noise abovea predetermined threshold.

In an aspect of the first embodiment, the signal artifacts detectionstep includes a Cone of Possibility Detection Method.

In an aspect of the first embodiment, the signal artifacts detectionstep includes evaluating the data stream for a non-physiologicalrate-of-change.

In an aspect of the first embodiment, the signal artifacts detectionstep includes monitoring the frequency content of the signal.

In an aspect of the first embodiment, the frequency-content monitoringstep includes performing an orthogonal basis function-based transform.

In an aspect of the first embodiment, the transform is a FourierTransform or a wavelet transform.

In an aspect of the first embodiment, the artifacts replacement stepincludes performing linear or non-linear regression.

In an aspect of the first embodiment, the artifacts replacement stepincludes performing a trimmed mean.

In an aspect of the first embodiment, the artifacts replacement stepincludes filtering using a non-recursive filter.

In an aspect of the first embodiment, the non-recursive filtering stepuses a finite impulse response filter.

In an aspect of the first embodiment, the artifacts replacement stepincludes filtering using a recursive filter.

In an aspect of the first embodiment, the recursive filtering step usesan infinite impulse response filter.

In an aspect of the first embodiment, the artifacts replacement stepincludes a performing a maximum average algorithm.

In an aspect of the first embodiment, the artifacts replacement stepincludes performing a Cone of Possibility Replacement Method.

In an aspect of the first embodiment, the method further includesestimating future glucose signal values based on historical glucosevalues.

In an aspect of the first embodiment, the glucose future estimation stepincludes algorithmically estimating the future signal value based usingat least one of linear regression, non-linear regression, and anauto-regressive algorithm.

In an aspect of the first embodiment, the glucose future estimation stepfurther includes measuring at least one of rate-of-change, acceleration,and physiologically feasibility of one or more signal values andsubsequently selectively applying the algorithm conditional on a rangeof one of the measurements.

In an aspect of the first embodiment, the glucose sensor includes anelectrochemical cell including working, counter, and referenceelectrodes, and wherein the artifacts replacement step includesnormalizing the data signal based on baseline drift at the referenceelectrode.

In an aspect of the first embodiment, the signal artifacts replacementstep is substantially continual.

In an aspect of the first embodiment, the signal artifacts replacementstep is initiated in response to positive detection of signal artifacts.

In an aspect of the first embodiment, the signal artifacts replacementstep is terminated in response to detection of negligible signalartifacts.

In an aspect of the first embodiment, the signal artifacts detectionstep includes evaluating the severity of the signal artifacts.

In an aspect of the first embodiment, the severity evaluation is basedon an amplitude of the transient non-glucose related signal artifacts.

In an aspect of the first embodiment, the severity evaluation is basedon a duration of the transient non-glucose related signal artifacts.

In an aspect of the first embodiment, the severity evaluation is basedon a rate-of-change of the transient non-glucose related signalartifacts.

In an aspect of the first embodiment, the severity evaluation is basedon a frequency content of the transient non-glucose related signalartifacts.

In an aspect of the first embodiment, the artifacts replacement stepincludes selectively applying one of a plurality of signal estimationalgorithm factors in response to the severity of the signal artifacts.

In an aspect of the first embodiment, the plurality of signal estimationalgorithm factors includes a single algorithm with a plurality ofparameters that are selectively applied to the algorithm.

In an aspect of the first embodiment, the plurality of signal estimationalgorithm factors includes a plurality of distinct algorithms.

In an aspect of the first embodiment, the step of selectively applyingone of a plurality of signal estimation algorithm factors includesselectively applying a predetermined algorithm that includes a set ofparameters whose values depend on the severity of the signal artifacts.

In an aspect of the first embodiment, the method further includesdiscarding at least some of the signal artifacts.

In an aspect of the first embodiment, the method further includesprojecting glucose signal values for a time during which no data isavailable.

In a second embodiment, a method is provided for processing data signalsobtained from a glucose sensor including: obtaining a data stream from aglucose sensor; detecting transient non-glucose related signal artifactsin the data stream that have a higher amplitude than a system noise; andselectively applying one of a plurality of signal estimation algorithmfactors to replace non-glucose related signal artifacts.

In an aspect of the second embodiment, the data signal obtaining stepincludes receiving data from one of non-invasive, minimally invasive,and invasive glucose sensor.

In an aspect of the second embodiment, the data signal obtaining stepincludes receiving data from one of an enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,iontophoretic, and radiometric glucose sensor.

In an aspect of the second embodiment, the data signal obtaining stepincludes receiving data from a wholly implantable glucose sensor.

In an aspect of the second embodiment, the signal artifacts detectionstep includes testing for ischemia within or proximal to the glucosesensor.

In an aspect of the second embodiment, the ischemia testing stepincludes obtaining oxygen concentration using an oxygen sensor proximalto or within the glucose sensor.

In an aspect of the second embodiment, the ischemia testing stepincludes comparing a measurement from an oxygen sensor proximal to orwithin the glucose sensor with a measurement from an oxygen sensordistal from the glucose sensor.

In an aspect of the second embodiment, the glucose sensor includes anelectrochemical cell including a working electrode and a referenceelectrode, and wherein the ischemia-testing step includes pulsedamperometric detection.

In an aspect of the second embodiment, the glucose sensor includes anelectrochemical cell including working, counter and referenceelectrodes, and wherein the ischemia-testing step includes monitoringthe counter electrode.

In an aspect of the second embodiment, the glucose sensor includes anelectrochemical cell including working, counter and referenceelectrodes, and wherein the ischemia testing step includes monitoringthe reference electrode.

In an aspect of the second embodiment, the glucose sensor includes anelectrochemical cell including an anode and a cathode, and wherein theischemia-testing step includes monitoring the cathode.

In an aspect of the second embodiment, the signal artifacts detectionstep includes monitoring a level of pH proximal to the sensor.

In an aspect of the second embodiment, the signal artifacts detectionstep includes monitoring a temperature proximal to the sensor.

In an aspect of the second embodiment, the signal artifacts detectionstep includes comparing a level of pH proximal to and distal to thesensor.

In an aspect of the second embodiment, the signal artifacts detectionstep includes comparing a temperature proximal to and distal to thesensor.

In an aspect of the second embodiment, the signal artifacts detectionstep includes monitoring the pressure or stress within the glucosesensor.

In an aspect of the second embodiment, the signal artifacts detectionstep includes evaluating historical data for high amplitude noise abovea predetermined threshold.

In an aspect of the second embodiment, the signal artifacts detectionstep includes a Cone of Possibility Detection Method.

In an aspect of the second embodiment, the signal artifacts detectionstep includes evaluating the signal for a non-physiologicalrate-of-change.

In an aspect of the second embodiment, the signal artifacts detectionstep includes monitoring the frequency content of the signal.

In an aspect of the second embodiment, the frequency-content monitoringstep includes performing an orthogonal basis function-based transform.

In an aspect of the second embodiment, the transform is a FourierTransform or a wavelet transform.

In an aspect of the second embodiment, the artifacts replacement stepincludes performing linear or non-linear regression.

In an aspect of the second embodiment, the artifacts replacement stepincludes performing a trimmed mean.

In an aspect of the second embodiment, the artifacts replacement stepincludes filtering using a non-recursive filter.

In an aspect of the second embodiment, the non-recursive filtering stepuses a finite impulse response filter.

In an aspect of the second embodiment, the artifacts replacement stepincludes filtering using a recursive filter.

In an aspect of the second embodiment, the recursive filtering step usesan infinite impulse response filter.

In an aspect of the second embodiment, the artifacts replacement stepincludes a performing a maximum average algorithm.

In an aspect of the second embodiment, the artifacts replacement stepincludes performing a Cone of Possibility algorithm.

In an aspect of the second embodiment, the method further includesestimating future glucose signal values based on historical glucosevalues.

In an aspect of the second embodiment, the glucose future estimationstep includes algorithmically estimating the future signal value basedusing at least one of linear regression, non-linear regression, and anauto-regressive algorithm.

In an aspect of the second embodiment, the glucose future estimationstep further includes measuring at least one of rate-of-change,acceleration, and physiologically feasibility of one or more signalvalues and subsequently selectively applying the algorithm conditionalon a range of one of the measurements.

In an aspect of the second embodiment, the glucose sensor includes anelectrochemical cell including working, counter, and referenceelectrodes, and wherein the artifacts replacement step includesnormalizing the data signal based on baseline drift at the referenceelectrode.

In an aspect of the second embodiment, the selective application step issubstantially continual.

In an aspect of the second embodiment, the selective application step isinitiated in response to positive detection of signal artifacts.

In an aspect of the second embodiment, the selective application step isterminated in response to detection of negligible signal artifacts.

In an aspect of the second embodiment, the signal artifacts detectionstep includes evaluating the severity of the signal artifacts.

In an aspect of the second embodiment, the severity evaluation is basedon an amplitude of the transient non-glucose related signal artifacts.

In an aspect of the second embodiment, the severity evaluation is basedon a duration of the transient non-glucose related signal artifacts.

In an aspect of the second embodiment, the severity evaluation is basedon a rate-of-change of the transient non-glucose related signalartifacts.

In an aspect of the second embodiment, the severity evaluation is basedon a frequency content of the transient non-glucose related signalartifacts.

In an aspect of the second embodiment, the selective application stepapplies the one of a plurality of signal estimation algorithm factors inresponse to the severity of the signal artifacts.

In an aspect of the second embodiment, the plurality of signalestimation algorithm factors includes a single algorithm with aplurality of parameters that are selectively applied to the algorithm.

In an aspect of the second embodiment, the plurality of signalestimation algorithm factors includes a plurality of distinctalgorithms.

In an aspect of the second embodiment, the selective application stepincludes selectively applying a predetermined algorithm that includes aset of parameters whose values depend on the severity of the signalartifacts.

In an aspect of the second embodiment, the method further includesdiscarding at least some of the signal artifacts.

In an aspect of the second embodiment, the selective application stepfurther includes projecting glucose signal values for a time duringwhich no data is available.

In a third embodiment, a system is provided for processing data signalsobtained from a glucose sensor, including: a signal processing moduleincluding programming to monitor a data stream from the sensor over aperiod of time; a detection module including programming to detecttransient non-glucose related signal artifacts in the data stream thathave a higher amplitude than a system noise; and a signal estimationmodule including programming to replace at least some of the signalartifacts with estimated glucose signal values.

In an aspect of the third embodiment, the signal processing module isadapted to receive data from one of non-invasive, minimally invasive,and invasive glucose sensor.

In an aspect of the third embodiment, the signal processing module isadapted to receive data from one of an enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,iontophoretic, and radiometric glucose sensor.

In an aspect of the third embodiment, the signal processing module isadapted to receive data from a wholly implantable glucose sensor.

In an aspect of the third embodiment, the detection module includesprogramming to for ischemia detection.

In an aspect of the third embodiment, the detection module includesprogramming to detect ischemia from a first oxygen sensor locatedproximal to or within the glucose sensor.

In an aspect of the third embodiment, the detection module furtherincludes programming to compare a measurement from a first oxygen sensorlocated proximal to or within the glucose sensor with a measurement froma second oxygen sensor located distal to the glucose sensor for ischemiadetection.

In an aspect of the third embodiment, the detection module furtherincludes programming to detect ischemia using pulsed amperometricdetection of an electrochemical cell including a working electrode and areference electrode.

In an aspect of the third embodiment, the detection module furtherincludes programming to detect ischemia by monitoring a counterelectrode of an electrochemical cell that includes working, counter andreference electrodes.

In an aspect of the third embodiment, the detection module furtherincludes programming to detect ischemia by monitoring a referenceelectrode of an electrochemical cell that includes working, counter andreference electrodes.

In an aspect of the third embodiment, the detection module furtherincludes programming to detect ischemia by monitoring a cathode of anelectrochemical cell.

In an aspect of the third embodiment, the detection module monitors alevel of pH proximal to the glucose sensor.

In an aspect of the third embodiment, the detection module monitors atemperature proximal to the glucose sensor.

In an aspect of the third embodiment, the detection module compares alevel of pH proximal to and distal to the sensor.

In an aspect of the third embodiment, the detection module compares atemperature proximal to and distal to the glucose sensor.

In an aspect of the third embodiment, the detection module monitors apressure or stress within the glucose sensor.

In an aspect of the third embodiment, the detection module evaluateshistorical data for high amplitude noise above a predeterminedthreshold.

In an aspect of the third embodiment, the detection module includesprogramming to perform a Cone of Possibility to detect signal artifacts.

In an aspect of the third embodiment, the detection module evaluates thedata stream for a non-physiological rate-of-change.

In an aspect of the third embodiment, the detection module monitors thefrequency content of the signal.

In an aspect of the third embodiment, the detection module monitors thefrequency content including performing an orthogonal basisfunction-based transform.

In an aspect of the third embodiment, the orthogonal basisfunction-based transform includes a Fourier Transform or a wavelettransform.

In an aspect of the third embodiment, the signal estimation moduleestimates glucose signal values using linear or non-linear regression.

In an aspect of the third embodiment, the signal estimation moduleestimates glucose signal values using a trimmed mean.

In an aspect of the third embodiment, the signal estimation moduleestimates glucose signal values using a non-recursive filter.

In an aspect of the third embodiment, the non-recursive filter is afinite impulse response filter.

In an aspect of the third embodiment, the signal estimation moduleestimates glucose signal values using a recursive filter.

In an aspect of the third embodiment, the recursive filter is aninfinite impulse response filter.

In an aspect of the third embodiment, the signal estimation moduleestimates glucose signal values using a maximum average algorithm.

In an aspect of the third embodiment, the signal estimation moduleestimates glucose signal values using a Cone of Possibility ReplacementMethod.

In an aspect of the third embodiment, the signal estimation modulefurther includes programming to estimate future glucose signal valuesbased on historical glucose values.

In an aspect of the third embodiment, the future glucose signal valueprogramming includes algorithmically estimating the future signal valuebased using at least one of linear regression, non-linear regression,and an auto-regressive algorithm.

In an aspect of the third embodiment, the signal estimation modulefurther includes programming to measure at least one of rate-of-change,acceleration, and physiologically feasibility of one or more signalvalues, and wherein the signal estimation module further includesprogramming to selectively apply an algorithm responsive to value of oneof the measurements from the detection module.

In an aspect of the third embodiment, signal estimation module includesprogramming to normalize the data stream based on baseline drift at areference electrode of a glucose sensor that includes an electrochemicalcell including working, counter, and reference electrodes.

In an aspect of the third embodiment, the signal estimation modulecontinually replaces the data stream with estimated signal values.

In an aspect of the third embodiment, the signal estimation moduleinitiates signal replacement of the data stream in response to positivedetection of signal artifacts.

In an aspect of the third embodiment, the signal estimation moduleterminates signal replacement in response to detection of negligiblesignal artifacts.

In an aspect of the third embodiment, the detection module evaluates theseverity of the signal artifacts.

In an aspect of the third embodiment, the detection module evaluates theseverity of the signal artifacts based on an amplitude of the transientnon-glucose related signal artifacts.

In an aspect of the third embodiment, the detection module evaluates theseverity of the signal artifacts based on a duration of the transientnon-glucose related signal artifacts.

In an aspect of the third embodiment, the detection module evaluates theseverity of the signal artifacts based on a rate-of-change of thetransient non-glucose related signal artifacts.

In an aspect of the third embodiment, the detection module evaluates theseverity of the signal artifacts based on a frequency content of thetransient non-glucose related signal artifacts.

In an aspect of the third embodiment, the signal estimation moduleincludes programming to selectively apply one of a plurality of signalestimation algorithm factors in response to the severity of the signalartifacts.

In an aspect of the third embodiment, the plurality of signal estimationalgorithm factors includes a single algorithm with a plurality ofparameters that are selectively applied to the algorithm.

In an aspect of the third embodiment, the plurality of signal estimationalgorithm factors includes a plurality of distinct algorithms.

In an aspect of the third embodiment, the signal estimation moduleselectively applies a set of parameters whose values depend on theseverity of the signal artifacts to one of a predetermined algorithm.

In an aspect of the third embodiment, the detection module includesprogramming to discard at least some of the signal artifacts.

In an aspect of the third embodiment, the signal estimation moduleincludes programming to project glucose signal values for a time duringwhich no data is available.

In a fourth embodiment, a system is provided for processing data signalsobtained from a glucose sensor, the system including: a signalprocessing module including programming to monitor a data stream fromthe sensor over a period of time; a detection module includingprogramming to detect transient non-glucose related signal artifacts inthe wherein the plurality of signal estimation algorithm factors includea plurality of distinct algorithms data streams that have a higheramplitude than a system noise; and a signal estimation module includingprogramming to selectively apply one of a plurality of signal estimationalgorithm factors to replace non-glucose related signal artifacts.

In an aspect of the fourth embodiment, the signal processing module isadapted to receive data from one of non-invasive, minimally invasive,and invasive glucose sensor.

In an aspect of the fourth embodiment, the signal processing module isadapted to receive data from one of an enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,iontophoretic, and radiometric glucose sensor.

In an aspect of the fourth embodiment, the signal processing module isadapted to receive data from a wholly implantable glucose sensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to detect ischemia within or proximal to the glucose sensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to obtain oxygen concentration using an oxygen sensorproximal to or within the glucose sensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to compare a measurement from an oxygen sensor proximal toor within the glucose sensor with a measurement from an oxygen sensordistal from the glucose sensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to detect ischemia using pulsed amperometric detection of anelectrochemical cell that includes a working electrode and a referenceelectrode.

In an aspect of the fourth embodiment, the detection module includesprogramming to monitor a counter electrode of a glucose sensor thatincludes an electrochemical cell including working, counter andreference electrodes.

In an aspect of the fourth embodiment, the detection module includesprogramming to monitor a reference electrode of a glucose sensor thatincludes an electrochemical cell including working, counter andreference electrodes.

In an aspect of the fourth embodiment, the detection module includesprogramming to monitor a cathode of a glucose sensor that includes anelectrochemical cell including an anode and a cathode.

In an aspect of the fourth embodiment, the detection module includesprogramming to monitor a level of pH proximal to the glucose sensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to monitor a temperature proximal to the glucose sensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to compare a level of pH proximal to and distal to theglucose sensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to compare a temperature proximal to and distal to thesensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to monitor a pressure or stress within the glucose sensor.

In an aspect of the fourth embodiment, the detection module includesprogramming to evaluate historical data for high amplitude noise above apredetermined threshold.

In an aspect of the fourth embodiment, the detection module includesprogramming to perform Cone of Possibility Detection.

In an aspect of the fourth embodiment, the detection module includesprogramming to evaluate the signal for a non-physiologicalrate-of-change.

In an aspect of the fourth embodiment, the detection module includesprogramming to monitor the frequency content of the signal.

In an aspect of the fourth embodiment, the detection module performs anorthogonal basis function-based transform to monitor frequency content.

In an aspect of the fourth embodiment, the transform is a FourierTransform or a wavelet transform.

In an aspect of the fourth embodiment, the signal estimation moduleestimates glucose signal values using linear or non-linear regression.

In an aspect of the fourth embodiment, the signal estimation moduleestimates glucose signal values using a trimmed mean.

In an aspect of the fourth embodiment, the signal estimation moduleestimates glucose signal values using a non-recursive filter.

In an aspect of the fourth embodiment, the non-recursive filter is afinite impulse response filter.

In an aspect of the fourth embodiment, the signal estimation moduleestimates glucose signal values using a recursive filter.

In an aspect of the fourth embodiment, the recursive filter is aninfinite impulse response filter.

In an aspect of the fourth embodiment, the signal estimation moduleestimates glucose signal values using a maximum average algorithm.

In an aspect of the fourth embodiment, the signal estimation moduleestimates glucose signal values using Cone of Possibility ReplacementMethod algorithm.

In an aspect of the fourth embodiment, the signal estimation modulefurther includes programming to estimate future glucose signal valuesbased on historical glucose values.

In an aspect of the fourth embodiment, future glucose signal valueprogramming includes algorithmically estimating the future signal valuebased using at least one of linear regression, non-linear regression,and an auto-regressive algorithm.

In an aspect of the fourth embodiment, the signal estimation modulefurther includes programming to measure at least one of rate-of-change,acceleration, and physiologically feasibility of one or more signalvalues, and wherein the signal estimation module further includesprogramming to selectively apply an algorithm responsive to value of oneof the measurements from the detection module.

In an aspect of the fourth embodiment, the signal estimation moduleincludes programming to normalize the data stream based on baselinedrift at a reference electrode of a glucose sensor that includes anelectrochemical cell including working, counter, and referenceelectrodes.

In an aspect of the fourth embodiment, the signal estimation modulecontinually replaces the data stream with estimated signal values.

In an aspect of the fourth embodiment, the signal estimation moduleinitiates signal replacement of the data stream in response to positivedetection of signal artifacts.

In an aspect of the fourth embodiment, the signal estimation moduleterminates signal replacement in response to detection of negligiblesignal artifacts.

In an aspect of the fourth embodiment, the detection module evaluatesthe severity of the signal artifacts.

In an aspect of the fourth embodiment, the detection module evaluatesthe severity of the signal artifacts based on an amplitude of thetransient non-glucose related signal artifacts.

In an aspect of the fourth embodiment, the detection module evaluatesthe severity of the signal artifacts based on a duration of thetransient non-glucose related signal artifacts.

In an aspect of the fourth embodiment, the detection module evaluatesthe severity of the signal artifacts based on a rate-of-change of thetransient non-glucose related signal artifacts.

In an aspect of the fourth embodiment, the detection module evaluatesthe severity of the signal artifacts based on a frequency content of thetransient non-glucose related signal artifacts.

In an aspect of the fourth embodiment, the signal estimation moduleincludes programming to selectively apply one of a plurality of signalestimation algorithm factors in response to the severity of the signalartifacts.

In an aspect of the fourth embodiment, the plurality of signalestimation algorithm factors includes a single algorithm with aplurality of parameters that are selectively applied to the algorithm.

In an aspect of the fourth embodiment, the plurality of signalestimation algorithm factors includes a plurality of distinctalgorithms.

In an aspect of the fourth embodiment, the signal estimation moduleselectively applies a set of parameters whose values depend on theseverity of the signal artifacts to one of a predetermined algorithm.

In an aspect of the fourth embodiment, the detection module includesprogramming to discard at least some of the signal artifacts.

In an aspect of the fourth embodiment, the signal estimation moduleincludes programming to project glucose signal values for a time duringwhich no data is available.

In a fifth embodiment, an implantable glucose monitoring device isprovided including: a glucose sensor; and a processor operatively linkedto the sensor designed to receive a data stream from the sensor; whereinthe processor is programmed to analyze the data stream and to detecttransient non-glucose related signal artifacts in the data stream thathave a higher amplitude than system noise, and to replace at least someof the signal artifacts with estimated values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exploded perspective view of a glucose sensor in oneembodiment.

FIG. 2 is a block diagram that illustrates sensor electronics in oneembodiment.

FIGS. 3A to 3D are schematic views of a receiver in first, second,third, and fourth embodiments, respectively.

FIG. 4 is a block diagram of receiver electronics in one embodiment.

FIG. 5 is a flow chart that illustrates the process of calibrating thesensor data in one embodiment.

FIG. 6 is a graph that illustrates a linear regression used to calibratethe sensor data in one embodiment.

FIG. 7A is a graph that shows a raw data stream obtained from a glucosesensor over a 4 hour time span in one example.

FIG. 7B is a graph that shows a raw data stream obtained from a glucosesensor over a 36 hour time span in another example.

FIG. 8 is a flow chart that illustrates the process of detecting andreplacing transient non-glucose related signal artifacts in a datastream in one embodiment.

FIG. 9 is a graph that illustrates the correlation between the counterelectrode voltage and signal artifacts in a data stream from a glucosesensor in one embodiment.

FIG. 10A is a circuit diagram of a potentiostat that controls a typicalthree-electrode system in one embodiment.

FIG. 10B is a diagram known as Cyclic-Voltammetry (CV) curve, whichillustrates the relationship between applied potential (V_(BIAS)) andsignal strength of the working electrode (I_(SENSE)) and can be used todetect signal artifacts.

FIG. 10C is a diagram showing a CV curve that illustrates an alternativeembodiment of signal artifacts detection, wherein pH and/or temperaturecan be monitoring using the CV curve.

FIG. 11 is a graph and spectrogram that illustrate the correlationbetween high frequency and signal artifacts observed by monitoring thefrequency content of a data stream in one embodiment.

FIG. 12 is a graph that illustrates a data stream obtained from aglucose sensor and a signal smoothed by trimmed linear regression thatcan be used to replace some of or the entire raw data stream in oneembodiment.

FIG. 13 is a graph that illustrates a data stream obtained from aglucose sensor and a FIR-smoothed data signal that can be used toreplace some of or the entire raw data stream in one embodiment.

FIG. 14 is a graph that illustrates a data stream obtained from aglucose sensor and an IIR-smoothed data signal that can be used toreplace some of or the entire raw data stream in one embodiment.

FIG. 15 is a flowchart that illustrates the process of selectivelyapplying signal estimation based on the severity of signal artifacts ona data stream.

FIG. 16 is a graph that illustrates selectively applying a signalestimation algorithm responsive to positive detection of signalartifacts on the raw data stream.

FIG. 17 is a graph that illustrates selectively applying a plurality ofsignal estimation algorithm factors responsive to a severity of signalartifacts on the raw data stream.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

Definitions

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “EEPROM,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, electrically erasableprogrammable read-only memory, which is user-modifiable read-only memory(ROM) that can be erased and reprogrammed (e.g., written to) repeatedlythrough the application of higher than normal electrical voltage.

The term “SRAM,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, static random accessmemory (RAM) that retains data bits in its memory as long as power isbeing supplied.

The term “A/D Converter,” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, hardware and/orsoftware that converts analog electrical signals into correspondingdigital signals.

The term “microprocessor,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation a computer systemor processor designed to perform arithmetic and logic operations usinglogic circuitry that responds to and processes the basic instructionsthat drive a computer.

The term “RF transceiver,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation, a radio frequencytransmitter and/or receiver for transmitting and/or receiving signals.

The term “jitter,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, noise above and below themean caused by ubiquitous noise caused by a circuit and/or environmentaleffects; jitter can be seen in amplitude, phase timing, or the width ofthe signal pulse.

The terms “raw data stream” and “data stream,” as used herein, are broadterms and are used in their ordinary sense, including, withoutlimitation, an analog or digital signal directly related to the measuredglucose from the glucose sensor. In one example, the raw data stream isdigital data in “counts” converted by an A/D converter from an analogsignal (e.g., voltage or amps) representative of a glucoseconcentration. The terms broadly encompass a plurality of time spaceddata points from a substantially continuous glucose sensor, whichcomprises individual measurements taken at time intervals ranging fromfractions of a second up to, e.g., 1, 2, or 5 minutes or longer.

The term “counts,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, a unit of measurement ofa digital signal. In one example, a raw data stream measured in countsis directly related to a voltage (e.g., converted by an A/D converter),which is directly related to current from the working electrode. Inanother example, counter electrode voltage measured in counts isdirectly related to a voltage.

The terms “glucose sensor” and “member for determining the amount ofglucose in a biological sample,” as used herein, are broad terms and areused in an ordinary sense, including, without limitation, any mechanism(e.g., enzymatic or non-enzymatic) by which glucose can be quantified.For example, some embodiments utilize a membrane that contains glucoseoxidase that catalyzes the conversion of oxygen and glucose to hydrogenperoxide and gluconate, as illustrated by the following chemicalreaction:

Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O2 and the product H2O2, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “operably connected” and “operably linked,” as used herein,are broad terms and are used in their ordinary sense, including, withoutlimitation, one or more components being linked to another component(s)in a manner that allows transmission of signals between the components.For example, one or more electrodes can be used to detect the amount ofglucose in a sample and convert that information into a signal, e.g., anelectrical or electromagnetic signal; the signal can then be transmittedto an electronic circuit. In this case, the electrode is “operablylinked” to the electronic circuitry. These terms are broad enough toinclude wireless connectivity.

The term “electronic circuitry,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, thecomponents of a device configured to process biological informationobtained from a host. In the case of a glucose-measuring device, thebiological information is obtained by a sensor regarding a particularglucose in a biological fluid, thereby providing data regarding theamount of that glucose in the fluid. U.S. Pat. Nos. 4,757,022, 5,497,772and 4,787,398, which are hereby incorporated by reference, describesuitable electronic circuits that can be utilized with devices includingthe biointerface membrane of a preferred embodiment.

The term “substantially” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, being largely but notnecessarily wholly that which is specified.

The term “proximal” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, near to a point ofreference such as an origin, a point of attachment, or the midline ofthe body. For example, in some embodiments of a glucose sensor, whereinthe glucose sensor is the point of reference, an oxygen sensor locatedproximal to the glucose sensor will be in contact with or nearby theglucose sensor such that their respective local environments are shared(e.g., levels of glucose, oxygen, pH, temperature, etc. are similar).

The term “distal” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, spaced relatively farfrom a point of reference, such as an origin or a point of attachment,or midline of the body. For example, in some embodiments of a glucosesensor, wherein the glucose sensor is the point of reference, an oxygensensor located distal to the glucose sensor will be sufficiently farfrom the glucose sensor such their respective local environments are notshared (e.g., levels of glucose, oxygen, pH, temperature, etc. may notbe similar).

The term “electrochemical cell,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, a device inwhich chemical energy is converted to electrical energy. Such a celltypically consists of two or more electrodes held apart from each otherand in contact with an electrolyte solution. Connection of theelectrodes to a source of direct electric current renders one of themnegatively charged and the other positively charged. Positive ions inthe electrolyte migrate to the negative electrode (cathode) and therecombine with one or more electrons, losing part or all of their chargeand becoming new ions having lower charge or neutral atoms or molecules;at the same time, negative ions migrate to the positive electrode(anode) and transfer one or more electrons to it, also becoming new ionsor neutral particles. The overall effect of the two processes is thetransfer of electrons from the negative ions to the positive ions, achemical reaction.

The term “potentiostat,” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, an electrical systemthat controls the potential between the working and reference electrodesof a three-electrode cell at a preset value. It forces whatever currentis necessary to flow between the working and counter electrodes to keepthe desired potential, as long as the needed cell voltage and current donot exceed the compliance limits of the potentiostat.

The term “electrical potential,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, theelectrical potential difference between two points in a circuit which isthe cause of the flow of a current.

The term “host,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, mammals, particularlyhumans.

The phrase “continuous glucose sensing,” as used herein, is a broad termand is used in its ordinary sense, including, without limitation, theperiod in which monitoring of plasma glucose concentration iscontinuously or continually performed, for example, at time intervalsranging from fractions of a second up to, e.g., 1, 2, or 5 minutes, orlonger.

The term “sensor head,” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, the region of amonitoring device responsible for the detection of a particular glucose.The sensor head generally comprises a non-conductive body, a workingelectrode (anode), a reference electrode and a counter electrode(cathode) passing through and secured within the body forming anelectrochemically reactive surface at one location on the body and anelectronic connection at another location on the body, and amulti-region membrane affixed to the body and covering theelectrochemically reactive surface. The counter electrode typically hasa greater electrochemically reactive surface area than the workingelectrode. During general operation of the sensor a biological sample(e.g., blood or interstitial fluid) or a portion thereof contacts(directly or after passage through one or more membranes or domains) anenzyme (e.g., glucose oxidase); the reaction of the biological sample(or portion thereof) results in the formation of reaction products thatallow a determination of the glucose level in the biological sample. Insome embodiments, the multi-region membrane includes an enzyme domain(e.g., glucose oxidase), and an electrolyte phase (e.g., a free-flowingliquid phase comprising an electrolyte-containing fluid, as describedfurther below).

The term “electrochemically reactive surface,” as used herein, is abroad term and is used in its ordinary sense, including, withoutlimitation, the surface of an electrode where an electrochemicalreaction takes place. In the case of the working electrode, the hydrogenperoxide produced by the enzyme catalyzed reaction of the glucose beingdetected reacts creating a measurable electronic current (e.g.,detection of glucose utilizing glucose oxidase produces H₂O₂ as a byproduct, H₂O₂ reacts with the surface of the working electrode producingtwo protons (2H⁺), two electrons (2e⁻) and one molecule of oxygen (O₂)which produces the electronic current being detected). In the case ofthe counter electrode, a reducible species, e.g., O₂ is reduced at theelectrode surface in order to balance the current being generated by theworking electrode.

The term “electronic connection,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, anyelectronic connection known to those in the art that can be utilized tointerface the sensor head electrodes with the electronic circuitry of adevice such as mechanical (e.g., pin and socket) or soldered.

The terms “operably connected” and “operably linked,” as used herein,are broad terms and are used in their ordinary sense, including, withoutlimitation, one or more components being linked to another component(s)in a manner that allows transmission of signals between the components,e.g., wired or wirelessly. For example, one or more electrodes can beused to detect the amount of analyte in a sample and convert thatinformation into a signal; the signal can then be transmitted to anelectronic circuit means. In this case, the electrode is “operablylinked” to the electronic circuitry.

The term “sensing membrane,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation, a permeable orsemi-permeable membrane that can be comprised of two or more domains andis typically constructed of materials of a few microns thickness ormore, which are permeable to oxygen and may or may not be permeable toglucose. In one example, the sensing membrane comprises an immobilizedglucose oxidase enzyme, which enables an electrochemical reaction tooccur to measure a concentration of glucose.

The term “biointerface membrane,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, a permeablemembrane that can be comprised of two or more domains and is typicallyconstructed of materials of a few microns thickness or more, which canbe placed over the sensor body to keep host cells (e.g., macrophages)from gaining proximity to, and thereby damaging, the sensing membrane orforming a barrier cell layer and interfering with the transport ofglucose across the tissue-device interface.

The term “Clarke Error Grid,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, an error gridanalysis, which evaluates the clinical significance of the differencebetween a reference glucose value and a sensor generated glucose value,taking into account 1) the value of the reference glucose measurement,2) the value of the sensor glucose measurement, 3) the relativedifference between the two values, and 4) the clinical significance ofthis difference. See Clarke et al., “Evaluating Clinical Accuracy ofSystems for Self-Monitoring of Blood Glucose,” Diabetes Care, Volume 10,Number 5, September-October 1987, which is incorporated by referenceherein in its entirety.

The term “physiologically feasible,” as used herein, is a broad term andis used in its ordinary sense, including, without limitation, thephysiological parameters obtained from continuous studies of glucosedata in humans and/or animals. For example, a maximal sustained rate ofchange of glucose in humans of about 4 to 5 mg/dL/min and a maximumacceleration of the rate of change of about 0.1 to 0.2 mg/dL/min/min aredeemed physiologically feasible limits. Values outside of these limitswould be considered non-physiological and likely a result of signalerror, for example. As another example, the rate of change of glucose islowest at the maxima and minima of the daily glucose range, which arethe areas of greatest risk in patient treatment, thus a physiologicallyfeasible rate of change can be set at the maxima and minima based oncontinuous studies of glucose data. As a further example, it has beenobserved that the best solution for the shape of the curve at any pointalong glucose signal data stream over a certain time period (e.g., about20 to 30 minutes) is a straight line, which can be used to setphysiological limits.

The term “ischemia,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, local and temporarydeficiency of blood supply due to obstruction of circulation to a part(e.g., sensor). Ischemia can be caused by mechanical obstruction (e.g.,arterial narrowing or disruption) of the blood supply, for example.

The term “system noise,” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, unwanted electronicor diffusion-related noise which can include Gaussian, motion-related,flicker, kinetic, or other white noise, for example.

The terms “signal artifacts” and “transient non-glucose related signalartifacts that have a higher amplitude than system noise,” as usedherein, are broad terms and are used in their ordinary sense, including,without limitation, signal noise that is caused by substantiallynon-glucose reaction rate-limiting phenomena, such as ischemia, pHchanges, temperature changes, pressure, and stress, for example. Signalartifacts, as described herein, are typically transient andcharacterized by a higher amplitude than system noise.

The terms “low noise,” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, noise thatsubstantially decreases signal amplitude.

The terms “high noise” and “high spikes,” as used herein, are broadterms and are used in their ordinary sense, including, withoutlimitation, noise that substantially increases signal amplitude.

The term “frequency content,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, the spectraldensity, including the frequencies contained within a signal and theirpower.

The term “spectral density,” as used herein, is a broad term and is usedin its ordinary sense, including, without limitation, power spectraldensity of a given bandwidth of electromagnetic radiation is the totalpower in this bandwidth divided by the specified bandwidth. Spectraldensity is usually expressed in Watts per Hertz (W/Hz).

The term “orthogonal transform,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, a generalintegral transform that is defined by g(α)=ƒ_(a) ^(b)f(t)K(α,t)dt, whereK(α,t) represents a set of orthogonal basis functions.

The term “Fourier Transform,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, a techniquefor expressing a waveform as a weighted sum of sines and cosines.

The term “Discrete Fourier Transform,” as used herein, is a broad termand is used in its ordinary sense, including, without limitation, aspecialized Fourier transform where the variables are discrete.

The term “wavelet transform,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, a transformwhich converts a signal into a series of wavelets, which in theoryallows signals processed by the wavelet transform to be stored moreefficiently than ones processed by Fourier transform. Wavelets can alsobe constructed with rough edges, to better approximate real-worldsignals.

The term “chronoamperometry,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, anelectrochemical measuring technique used for electrochemical analysis orfor the determination of the kinetics and mechanism of electrodereactions. A fast-rising potential pulse is enforced on the working (orreference) electrode of an electrochemical cell and the current flowingthrough this electrode is measured as a function of time.

The term “pulsed amperometric detection,” as used herein, is a broadterm and is used in its ordinary sense, including, without limitation,an electrochemical flow cell and a controller, which applies thepotentials and monitors current generated by the electrochemicalreactions. The cell can include one or multiple working electrodes atdifferent applied potentials. Multiple electrodes can be arranged sothat they face the chromatographic flow independently (parallelconfiguration), or sequentially (series configuration).

The term “linear regression,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, finding aline in which a set of data has a minimal measurement from that line.Byproducts of this algorithm include a slope, a y-intercept, and anR-Squared value that determine how well the measurement data fits theline.

The term “non-linear regression,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, fitting a setof data to describe the relationship between a response variable and oneor more explanatory variables in a non-linear fashion.

The term “mean,” as used herein, is a broad term and is used in itsordinary sense, including, without limitation, the sum of theobservations divided by the number of observations.

The term “trimmed mean,” as used herein, is a broad term and is used inits ordinary sense, including, without limitation, a mean taken afterextreme values in the tails of a variable (e.g., highs and lows) areeliminated or reduced (e.g., “trimmed”). The trimmed mean compensatesfor sensitivities to extreme values by dropping a certain percentage ofvalues on the tails. For example, the 50% trimmed mean is the mean ofthe values between the upper and lower quartiles. The 90% trimmed meanis the mean of the values after truncating the lowest and highest 5% ofthe values. In one example, two highest and two lowest measurements areremoved from a data set and then the remaining measurements areaveraged.

The term “non-recursive filter,” as used herein, is a broad term and isused in its ordinary sense, including, without limitation, an equationthat uses moving averages as inputs and outputs.

The terms “recursive filter” and “auto-regressive algorithm,” as usedherein, are broad terms and are used in their ordinary sense, including,without limitation, an equation in which includes previous averages arepart of the next filtered output. More particularly, the generation of aseries of observations whereby the value of each observation is partlydependent on the values of those that have immediately preceded it. Oneexample is a regression structure in which lagged response values assumethe role of the independent variables.

The term “signal estimation algorithm factors,” as used herein, is abroad term and is used in its ordinary sense, including, withoutlimitation, one or more algorithms that use historical and/or presentsignal data stream values to estimate unknown signal data stream values.For example, signal estimation algorithm factors can include one or morealgorithms, such as linear or non-linear regression. As another example,signal estimation algorithm factors can include one or more sets ofcoefficients that can be applied to one algorithm.

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

Overview

The preferred embodiments relate to the use of a glucose sensor thatmeasures a concentration of glucose or a substance indicative of theconcentration or presence of the glucose. In some embodiments, theglucose sensor is a continuous device, for example a subcutaneous,transdermal, or intravascular device. In some embodiments, the devicecan analyze a plurality of intermittent blood samples. The glucosesensor can use any method of glucose-measurement, including enzymatic,chemical, physical, electrochemical, spectrophotometric, polarimetric,calorimetric, iontophoretic, radiometric, or the like.

The glucose sensor can use any known method, including invasive,minimally invasive, and non-invasive sensing techniques, to provide adata stream indicative of the concentration of glucose in a host. Thedata stream is typically a raw data signal that is used to provide auseful value of glucose to a user, such as a patient or doctor, who maybe using the sensor. It is well known that raw data streams typicallyinclude system noise such as defined herein; however the preferredembodiments address the detection and replacement of “signal artifacts”as defined herein. Accordingly, appropriate signal estimation (e.g.,filtering, data smoothing, augmenting, projecting, and/or other methods)replace such erroneous signals (e.g., signal artifacts) in the raw datastream.

Glucose Sensor

The glucose sensor can be any device capable of measuring theconcentration of glucose. One exemplary embodiment is described below,which utilizes an implantable glucose sensor. However, it should beunderstood that the devices and methods described herein can be appliedto any device capable of detecting a concentration of glucose andproviding an output signal that represents the concentration of glucose.

FIG. 1 is an exploded perspective view of one exemplary embodimentcomprising an implantable glucose sensor 10 that utilizes amperometricelectrochemical sensor technology to measure glucose concentration. Inthis exemplary embodiment, a body 12 and head 14 house the electrodes 16and sensor electronics, which are described in more detail below withreference to FIG. 2. Three electrodes 16 are operably connected to thesensor electronics (FIG. 1) and are covered by a sensing membrane 17 anda biointerface membrane 18, which are attached by a clip 19.

In one embodiment, the three electrodes 16, which protrude through thehead 14, include a platinum working electrode, a platinum counterelectrode, and a silver/silver chloride reference electrode. The topends of the electrodes are in contact with an electrolyte phase (notshown), which is a free-flowing fluid phase disposed between the sensingmembrane 17 and the electrodes 16. The sensing membrane 17 includes anenzyme, e.g., glucose oxidase, which covers the electrolyte phase. Thebiointerface membrane 18 covers the sensing membrane 17 and serves, atleast in part, to protect the sensor 10 from external forces that canresult in environmental stress cracking of the sensing membrane 17.

In the illustrated embodiment, the counter electrode is provided tobalance the current generated by the species being measured at theworking electrode. In the case of a glucose oxidase based glucosesensor, the species being measured at the working electrode is H₂O₂.Glucose oxidase catalyzes the conversion of oxygen and glucose tohydrogen peroxide and gluconate according to the following reaction:

Glucose+O₂→Gluconate+H₂O₂

The change in H₂O₂ can be monitored to determine glucose concentrationbecause for each glucose molecule metabolized, there is a proportionalchange in the product H₂O₂. Oxidation of H₂O₂ by the working electrodeis balanced by reduction of ambient oxygen, enzyme generated H₂O₂, orother reducible species at the counter electrode. The H₂O₂ produced fromthe glucose oxidase reaction further reacts at the surface of workingelectrode and produces two protons (2H⁺), two electrons (2e⁻), and oneoxygen molecule (O₂).

In one embodiment, a potentiostat is employed to monitor theelectrochemical reaction at the electrochemical cell. The potentiostatapplies a constant potential to the working and reference electrodes todetermine a current value. The current that is produced at the workingelectrode (and flows through the circuitry to the counter electrode) isproportional to the amount of H₂O₂ that diffuses to the workingelectrode. Accordingly, a raw signal can be produced that isrepresentative of the concentration of glucose in the user's body, andtherefore can be utilized to estimate a meaningful glucose value, suchas described herein.

One problem with raw data stream output of enzymatic glucose sensorssuch as described above is caused by transient non-glucose reactionrate-limiting phenomenon. For example, if oxygen is deficient, relativeto the amount of glucose, then the enzymatic reaction will be limited byoxygen rather than glucose. Consequently, the output signal will beindicative of the oxygen concentration rather than the glucoseconcentration, producing erroneous signals. Other non-glucose reactionrate-limiting phenomenon could include temperature and/or pH changes,for example. Accordingly, reduction of signal noise, and particularlyreplacement of transient non-glucose related signal artifacts in thedata stream that have a higher amplitude than system noise, can beperformed in the sensor and/or in the receiver, such as described inmore detail below in the sections entitled “Signal Artifacts Detection”and “Signal Artifacts Replacement.”

FIG. 2 is a block diagram that illustrates one possible configuration ofthe sensor electronics in one embodiment. In this embodiment, apotentiostat 20 is shown, which is operatively connected to electrodes16 (FIG. 1) to obtain a current value, and includes a resistor (notshown) that translates the current into voltage. An A/D converter 21digitizes the analog signal into “counts” for processing. Accordingly,the resulting raw data stream in counts is directly related to thecurrent measured by the potentiostat 20.

A microprocessor 22 is the central control unit that houses EEPROM 23and SRAM 24, and controls the processing of the sensor electronics. Itis noted that certain alternative embodiments can utilize a computersystem other than a microprocessor to process data as described herein.In other alternative embodiments, an application-specific integratedcircuit (ASIC) can be used for some or all the sensor's centralprocessing. The EEPROM 23 provides semi-permanent storage of data, forexample, storing data such as sensor identifier (ID) and programming toprocess data streams (e.g., programming for signal artifacts detectionand/or replacement such as described elsewhere herein). The SRAM 24 canbe used for the system's cache memory, for example for temporarilystoring recent sensor data.

A battery 25 is operatively connected to the microprocessor 22 andprovides the necessary power for the sensor 10. In one embodiment, thebattery is a Lithium Manganese Dioxide battery, however anyappropriately sized and powered battery can be used (e.g., AAA,Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metal hydride,Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, orhermetically-sealed). In some embodiments the battery is rechargeable.In some embodiments, a plurality of batteries can be used to power thesystem. A Quartz Crystal 26 is operatively connected to themicroprocessor 22 and maintains system time for the computer system as awhole.

An RF Transceiver 27 is operably connected to the microprocessor 22 andtransmits the sensor data from the sensor 10 to a receiver (see FIGS. 3and 4). Although an RF transceiver is shown here, some other embodimentscan include a wired rather than wireless connection to the receiver. Inyet other embodiments, the receiver can be transcutaneously powered viaan inductive coupling, for example. A second quartz crystal 28 providesthe system time for synchronizing the data transmissions from the RFtransceiver. It is noted that the transceiver 27 can be substituted witha transmitter in other embodiments.

In some embodiments, a Signal Artifacts Detector 29 includes one or moreof the following: an oxygen detector 29 a, a pH detector 29 b, atemperature detector 29 c, and a pressure/stress detector 29 d, which isdescribed in more detail with reference to signal artifacts detection.It is noted that in some embodiments the signal artifacts detector 29 isa separate entity (e.g., temperature detector) operatively connected tothe microprocessor, while in other embodiments, the signal artifactsdetector is a part of the microprocessor and utilizes readings from theelectrodes, for example, to detect ischemia and other signal artifacts.

Receiver

FIGS. 3A to 3D are schematic views of a receiver 30 includingrepresentations of estimated glucose values on its user interface infirst, second, third, and fourth embodiments, respectively. The receiver30 comprises systems to receive, process, and display sensor data fromthe glucose sensor 10, such as described herein. Particularly, thereceiver 30 can be a pager-sized device, for example, and comprise auser interface that has a plurality of buttons 32 and a liquid crystaldisplay (LCD) screen 34, and which can optionally include a backlight.In some embodiments, the user interface can also include a keyboard, aspeaker, and a vibrator, as described below with reference to FIG. 4.

FIG. 3A illustrates a first embodiment wherein the receiver 30 shows anumeric representation of the estimated glucose value on its userinterface, which is described in more detail elsewhere herein.

FIG. 3B illustrates a second embodiment wherein the receiver 30 shows anestimated glucose value and approximately one hour of historical trenddata on its user interface, which is described in more detail elsewhereherein.

FIG. 3C illustrates a third embodiment wherein the receiver 30 shows anestimated glucose value and approximately three hours of historicaltrend data on its user interface, which is described in more detailelsewhere herein.

FIG. 3D illustrates a fourth embodiment wherein the receiver 30 shows anestimated glucose value and approximately nine hours of historical trenddata on its user interface, which is described in more detail elsewhereherein.

In some embodiments, a user can toggle through some or all of thescreens shown in FIGS. 3A to 3D using a toggle button on the receiver.In some embodiments, the user will be able to interactively select thetype of output displayed on their user interface. In other embodiments,the sensor output can have alternative configurations.

FIG. 4 is a block diagram that illustrates one possible configuration ofthe receiver's 30 electronics. It is noted that the receiver 30 cancomprise a configuration such as described with reference to FIGS. 3A to3D, above. Alternatively, the receiver 30 can comprise otherconfigurations, including a desktop computer, laptop computer, apersonal digital assistant (PDA), a server (local or remote to thereceiver), or the like. In some embodiments, the receiver 30 can beadapted to connect (via wired or wireless connection) to a desktopcomputer, laptop computer, PDA, server (local or remote to thereceiver), or the like, in order to download data from the receiver 30.In some alternative embodiments, the receiver 30 can be housed within ordirectly connected to the sensor 10 in a manner that allows sensor andreceiver electronics to work directly together and/or share dataprocessing resources. Accordingly, the receiver's electronics can begenerally referred to as a “computer system.”

A quartz crystal 40 is operatively connected to an RF transceiver 41that together function to receive and synchronize data streams (e.g.,raw data streams transmitted from the RF transceiver). Once received, amicroprocessor 42 processes the signals, such as described below.

The microprocessor 42 is the central control unit that provides theprocessing, such as calibration algorithms stored within EEPROM 43. TheEEPROM 43 is operatively connected to the microprocessor 42 and providessemi-permanent storage of data, storing data such as receiver ID andprogramming to process data streams (e.g., programming for performingcalibration and other algorithms described elsewhere herein). SRAM 44 isused for the system's cache memory and is helpful in data processing.

A battery 45 is operatively connected to the microprocessor 42 andprovides power for the receiver. In one embodiment, the battery is astandard AAA alkaline battery, however any appropriately sized andpowered battery can be used. In some embodiments, a plurality ofbatteries can be used to power the system. A quartz crystal 46 isoperatively connected to the microprocessor 42 and maintains system timefor the computer system as a whole.

A user interface 47 comprises a keyboard 2, speaker 3, vibrator 4,backlight 5, liquid crystal display (LCD 6), and one or more buttons 7.The components that comprise the user interface 47 provide controls tointeract with the user. The keyboard 2 can allow, for example, input ofuser information about himself/herself, such as mealtime, exercise,insulin administration, and reference glucose values. The speaker 3 canprovide, for example, audible signals or alerts for conditions such aspresent and/or predicted hyper- and hypoglycemic conditions. Thevibrator 4 can provide, for example, tactile signals or alerts forreasons such as described with reference to the speaker, above. Thebacklight 5 can be provided, for example, to aid the user in reading theLCD in low light conditions. The LCD 6 can be provided, for example, toprovide the user with visual data output such as is illustrated in FIGS.3A to 3D. The buttons 7 can provide for toggle, menu selection, optionselection, mode selection, and reset, for example.

Communication ports, including a PC communication (com) port 48 and areference glucose monitor com port 49 can be provided to enablecommunication with systems that are separate from, or integral with, thereceiver 30. The PC com port 48, for example, a serial communicationsport, allows for communicating with another computer system (e.g., PC,PDA, server, or the like). In one exemplary embodiment, the receiver 30is able to download historical data to a physician's PC forretrospective analysis by the physician. The reference glucose monitorcom port 49 allows for communicating with a reference glucose monitor(not shown) so that reference glucose values can be downloaded into thereceiver 30, for example, automatically. In one embodiment, thereference glucose monitor is integral with the receiver 30, and thereference glucose com port 49 allows internal communication between thetwo integral systems. In another embodiment, the reference glucosemonitor com port 49 allows a wireless or wired connection to referenceglucose monitor such as a self-monitoring blood glucose monitor (e.g.,for measuring finger stick blood samples).

Calibration

Reference is now made to FIG. 5, which is a flow chart that illustratesthe process of initial calibration and data output of the glucose sensor10 in one embodiment.

Calibration of the glucose sensor 10 comprises data processing thatconverts a sensor data stream into an estimated glucose measurement thatis meaningful to a user. Accordingly, a reference glucose value can beused to calibrate the data stream from the glucose sensor 10.

At block 51, a sensor data receiving module, also referred to as thesensor data module, receives sensor data (e.g., a data stream),including one or more time-spaced sensor data points, from a sensor viathe receiver, which can be in wired or wireless communication with thesensor. Some or all of the sensor data point(s) can be replaced byestimated signal values to address signal noise such as described inmore detail elsewhere herein. It is noted that during the initializationof the sensor, prior to initial calibration, the receiver 30 (e.g.,computer system) receives and stores the sensor data, however it may notdisplay any data to the user until initial calibration and eventuallystabilization of the sensor 10 has been determined.

At block 52, a reference data receiving module, also referred to as thereference input module, receives reference data from a reference glucosemonitor, including one or more reference data points. In one embodiment,the reference glucose points can comprise results from a self-monitoredblood glucose test (e.g., from a finger stick test). In one suchembodiment, the user can administer a self-monitored blood glucose testto obtain glucose value (e.g., point) using any known glucose sensor,and enter the numeric glucose value into the computer system. In anothersuch embodiment, a self-monitored blood glucose test comprises a wiredor wireless connection to the receiver 30 (e.g. computer system) so thatthe user simply initiates a connection between the two devices, and thereference glucose data is passed or downloaded between theself-monitored blood glucose test and the receiver 30. In yet anothersuch embodiment, the self-monitored glucose test is integral with thereceiver 30 so that the user simply provides a blood sample to thereceiver 30, and the receiver 30 runs the glucose test to determine areference glucose value.

Certain acceptability parameters can be set for reference valuesreceived from the user. For example, in one embodiment, the receiver mayonly accept reference glucose values between about 40 and about 400mg/dL.

At block 53, a data matching module, also referred to as the processormodule, matches reference data (e.g., one or more reference glucose datapoints) with substantially time corresponding sensor data (e.g., one ormore sensor data points) to provide one or more matched data pairs. Inone embodiment, one reference data point is matched to one timecorresponding sensor data point to form a matched data pair. In anotherembodiment, a plurality of reference data points are averaged (e.g.,equally or non-equally weighted average, mean-value, median, or thelike) and matched to one time corresponding sensor data point to form amatched data pair. In another embodiment, one reference data point ismatched to a plurality of time corresponding sensor data points averagedto form a matched data pair. In yet another embodiment, a plurality ofreference data points are averaged and matched to a plurality of timecorresponding sensor data points averaged to form a matched data pair.

In one embodiment, a time corresponding sensor data comprises one ormore sensor data points that occur, for example, 15±5 min after thereference glucose data timestamp (e.g., the time that the referenceglucose data is obtained). In this embodiment, the 15 minute time delayhas been chosen to account for an approximately 10 minute delayintroduced by the filter used in data smoothing and an approximately 5minute physiological time-lag (e.g., the time necessary for the glucoseto diffusion through a membrane(s) of an glucose sensor). In alternativeembodiments, the time corresponding sensor value can be more or lessthan in the above-described embodiment, for example ±60 minutes.Variability in time correspondence of sensor and reference data can beattributed to, for example, a longer or shorter time delay introducedduring signal estimation, or if the configuration of the glucose sensor10 incurs a greater or lesser physiological time lag.

In some practical implementations of the sensor 10, the referenceglucose data can be obtained at a time that is different from the timethat the data is input into the receiver 30. Accordingly, it should benoted that the “time stamp” of the reference glucose (e.g., the time atwhich the reference glucose value was obtained) may not be the same asthe time at which the receiver 30 obtained the reference glucose data.Therefore, some embodiments include a time stamp requirement thatensures that the receiver 30 stores the accurate time stamp for eachreference glucose value, that is, the time at which the reference valuewas actually obtained from the user.

In some embodiments, tests are used to evaluate the best-matched pairusing a reference data point against individual sensor values over apredetermined time period (e.g., about 30 minutes). In one suchembodiment, the reference data point is matched with sensor data pointsat 5-minute intervals and each matched pair is evaluated. The matchedpair with the best correlation can be selected as the matched pair fordata processing. In some alternative embodiments, matching a referencedata point with an average of a plurality of sensor data points over apredetermined time period can be used to form a matched pair.

At block 54, a calibration set module, also referred to as the processormodule, forms an initial calibration set from a set of one or morematched data pairs, which are used to determine the relationship betweenthe reference glucose data and the sensor glucose data, such asdescribed in more detail with reference to block 55, below.

The matched data pairs, which make up the initial calibration set, canbe selected according to predetermined criteria. In some embodiments,the number (n) of data pair(s) selected for the initial calibration setis one. In other embodiments, n data pairs are selected for the initialcalibration set wherein n is a function of the frequency of the receivedreference data points. In one exemplary embodiment, six data pairs makeup the initial calibration set.

In some embodiments, the data pairs are selected only within a certainglucose value threshold, for example wherein the reference glucose valueis between about 40 and about 400 mg/dL. In some embodiments, the datapairs that form the initial calibration set are selected according totheir time stamp.

At block 55, the conversion function module uses the calibration set tocreate a conversion function. The conversion function substantiallydefines the relationship between the reference glucose data and theglucose sensor data. A variety of known methods can be used with thepreferred embodiments to create the conversion function from thecalibration set. In one embodiment, wherein a plurality of matched datapoints form the initial calibration set, a linear least squaresregression is performed on the initial calibration set such as describedin more detail with reference to FIG. 6.

At block 56, a sensor data transformation module uses the conversionfunction to transform sensor data into substantially real-time glucosevalue estimates, also referred to as calibrated data, as sensor data iscontinuously (or intermittently) received from the sensor. In otherwords, the offset value at any given point in time can be subtractedfrom the raw value (e.g., in counts) and divided by the slope to obtainthe estimated glucose value:

${{mg}\text{/}{dL}} = \frac{\left( {{rawvalue} - {offset}} \right)}{slope}$

In some alternative embodiments, the sensor and/or reference glucosevalues are stored in a database for retrospective analysis.

At block 57, an output module provides output to the user via the userinterface. The output is representative of the estimated glucose value,which is determined by converting the sensor data into a meaningfulglucose value such as described in more detail with reference to block56, above. User output can be in the form of a numeric estimated glucosevalue, an indication of directional trend of glucose concentration,and/or a graphical representation of the estimated glucose data over aperiod of time, for example. Other representations of the estimatedglucose values are also possible, for example audio and tactile.

In one embodiment, such as shown in FIG. 3A, the estimated glucose valueis represented by a numeric value. In other exemplary embodiments, suchas shown in FIGS. 3B to 3D, the user interface graphically representsthe estimated glucose data trend over predetermined a time period (e.g.,one, three, and nine hours, respectively). In alternative embodiments,other time periods can be represented.

Accordingly, after initial calibration of the sensor, real-timecontinuous glucose information can be displayed on the user interface sothat the user can regularly and proactively care for his/her diabeticcondition within the bounds set by his/her physician.

In alternative embodiments, the conversion function is used to predictglucose values at future points in time. These predicted values can beused to alert the user of upcoming hypoglycemic or hyperglycemic events.Additionally, predicted values can be used to compensate for the timelag (e.g., 15 minute time lag such as described elsewhere herein), sothat an estimated glucose value displayed to the user represents theinstant time, rather than a time delayed estimated value.

In some embodiments, the substantially real-time estimated glucosevalue, a predicted future estimated glucose value, a rate of change,and/or a directional trend of the glucose concentration is used tocontrol the administration of a constituent to the user, including anappropriate amount and time, in order to control an aspect of the user'sbiological system. One such example is a closed loop glucose sensor andinsulin pump, wherein the glucose data (e.g., estimated glucose value,rate of change, and/or directional trend) from the glucose sensor isused to determine the amount of insulin, and time of administration,that can be given to a diabetic user to evade hyper- and hypoglycemicconditions.

FIG. 6 is a graph that illustrates one embodiment of a regressionperformed on a calibration set to create a conversion function such asdescribed with reference to FIG. 5, block 55, above. In this embodiment,a linear least squares regression is performed on the initialcalibration set. The x-axis represents reference glucose data; they-axis represents sensor data. The graph pictorially illustratesregression of matched pairs 66 in the calibration set. The regressioncalculates a slope 62 and an offset 64, for example, using thewell-known slope-intercept equation (y=mx+b), which defines theconversion function.

In alternative embodiments, other algorithms could be used to determinethe conversion function, for example forms of linear and non-linearregression, for example fuzzy logic, neural networks, piece-wise linearregression, polynomial fit, genetic algorithms, and other patternrecognition and signal estimation techniques.

In yet other alternative embodiments, the conversion function cancomprise two or more different optimal conversions because an optimalconversion at any time is dependent on one or more parameters, such astime of day, calories consumed, exercise, or glucose concentration aboveor below a set threshold, for example. In one such exemplary embodiment,the conversion function is adapted for the estimated glucoseconcentration (e.g., high vs. low). For example in an implantableglucose sensor it has been observed that the cells surrounding theimplant will consume at least a small amount of glucose as it diffusestoward the glucose sensor. Assuming the cells consume substantially thesame amount of glucose whether the glucose concentration is low or high,this phenomenon will have a greater effect on the concentration ofglucose during low blood sugar episodes than the effect on theconcentration of glucose during relatively higher blood sugar episodes.Accordingly, the conversion function can be adapted to compensate forthe sensitivity differences in blood sugar level. In one implementation,the conversion function comprises two different regression lines,wherein a first regression line is applied when the estimated bloodglucose concentration is at or below a certain threshold (e.g., 150mg/dL) and a second regression line is applied when the estimated bloodglucose concentration is at or above a certain threshold (e.g., 150mg/dL). In one alternative implementation, a predetermined pivot of theregression line that forms the conversion function can be applied whenthe estimated blood is above or below a set threshold (e.g., 150 mg/dL),wherein the pivot and threshold are determined from a retrospectiveanalysis of the performance of a conversion function and its performanceat a range of glucose concentrations. In another implementation, theregression line that forms the conversion function is pivoted about apoint in order to comply with clinical acceptability standards (e.g.,Clarke Error Grid, Consensus Grid, mean absolute relative difference, orother clinical cost function). Although only a few exampleimplementations are described, other embodiments include numerousimplementations wherein the conversion function is adaptively appliedbased on one or more parameters that can affect the sensitivity of thesensor data over time.

Additional methods for processing sensor glucose data are disclosed incopending U.S. patent application Ser. No. 10/633,367 filed Aug. 1, 2003and entitled, “SYSTEM AND METHODS FOR PROCESSING ANALYTE SENSOR DATA,”which is incorporated herein by reference in its entirety. In view ofthe above-described data processing, it should be obvious that improvingthe accuracy of the data stream will be advantageous for improvingoutput of glucose sensor data. Accordingly, the following description isrelated to improving data output by decreasing signal artifacts on theraw data stream from the sensor. The data smoothing methods of preferredembodiments can be employed in conjunction with any sensor or monitormeasuring levels of an analyte in vivo, wherein the level of the analytefluctuates over time, including but not limited to such sensors asdescribed in U.S. Pat. No. 6,001,067 to Shults et al.; U.S. PatentApplication 2003/0023317 to Brauker et al.; U.S. Pat. No. 6,212,416 toWard et al.; U.S. Pat. No. 6,119,028 to Schulman et al; U.S. Pat. No.6,400,974 to Lesho; U.S. Pat. No. 6,595,919 to Berner et al.; U.S. Pat.No. 6,141,573 to Kurnik et al.; U.S. Pat. No. 6,122,536 to Sun et al.;European Patent Application EP 1153571 to Varall et al.; U.S. Pat. No.6,512,939 to Colvin et al.; U.S. Pat. No. 5,605,152 to Slate et al.;U.S. Pat. No. 4,431,004 to Bessman et al.; U.S. Pat. No. 4,703,756 toGough et al; U.S. Pat. No. 6,514,718 to Heller et al; and U.S. Pat. No.5,985,129 to Gough et al., each of which are incorporated in thereentirety herein by reference.

Signal Artifacts

Typically, a glucose sensor produces a data stream that is indicative ofthe glucose concentration of a host, such as described in more detailabove. However, it is well known that the above described glucose sensoris only one example of an abundance of glucose sensors that are able toprovide raw data output indicative of the concentration of glucose.Thus, it should be understood that the systems and methods describedherein, including signal artifacts detection, signal artifactsreplacement, and other data processing, can be applied to a data streamobtained from any glucose sensor.

Raw data streams typically have some amount of “system noise,” caused byunwanted electronic or diffusion-related noise that degrades the qualityof the signal and thus the data. Accordingly, conventional glucosesensors are known to smooth raw data using methods that filter out thissystem noise, and the like, in order to improve the signal to noiseratio, and thus data output. One example of a conventionaldata-smoothing algorithm includes a finite impulse response filter(FIR), which is particularly suited for reducing high-frequency noise(see Steil et al. U.S. Pat. No. 6,558,351).

FIGS. 7A and 7B are graphs of raw data streams from an implantableglucose sensor prior to data smoothing. FIG. 7A is a graph that shows araw data stream obtained from a glucose sensor over an approximately 4hour time span in one example. FIG. 7B is a graph that shows a raw datastream obtained from a glucose sensor over an approximately 36 hour timespan in another example. The x-axis represents time in minutes. They-axis represents sensor data in counts. In these examples, sensoroutput in counts is transmitted every 30-seconds.

The “system noise” such as shown in sections 72 a, 72 b of the datastreams of FIGS. 7A and 7B, respectively, illustrate time periods duringwhich system noise can be seen on the data stream. This system noise canbe characterized as Gaussian, Brownian, and/or linear noise, and can besubstantially normally distributed about the mean. The system noise islikely electronic and diffusion-related, or the like, and can besmoothed using techniques such as by using an FIR filter. The systemnoise such as shown in the data of sections 72 a, 72 b is a fairlyaccurate representation of glucose concentration and can be confidentlyused to report glucose concentration to the user when appropriatelycalibrated.

The “signal artifacts” such as shown in sections 74 a, 74 b of the datastream of FIGS. 7A and 7B, respectively, illustrate time periods duringwhich “signal artifacts” can be seen, which are significantly differentfrom the previously described system noise (sections 72 a, 72 b). Thisnoise, such as shown in section 74 a and 74 b, is referred to herein as“signal artifacts” and more particularly described as “transientnon-glucose dependent signal artifacts that have a higher amplitude thansystem noise.” At times, signal artifacts comprise low noise, whichgenerally refers to noise that substantially decreases signal amplitude76 a, 76 b herein, which is best seen in the signal artifacts 74 b ofFIG. 7B. Occasional high spikes 78 a, 78 b, which generally correspondto noise that substantially increases signal amplitude, can also be seenin the signal artifacts, which generally occur after a period of lownoise. These high spikes are generally observed after transient lownoise and typically result after reaction rate-limiting phenomena occur.For example, in an embodiment where a glucose sensor requires anenzymatic reaction, local ischemia creates a reaction that israte-limited by oxygen, which is responsible for low noise. In thissituation, glucose would be expected to build up in the membrane becauseit would not be completely catabolized during the oxygen deficit. Whenoxygen is again in excess, there would also be excess glucose due to thetransient oxygen deficit. The enzyme rate would speed up for a shortperiod until the excess glucose is catabolized, resulting in high noise.

Analysis of signal artifacts such as shown sections 74 a, 74 b of FIGS.7A and 7B, respectively, indicates that the observed low noise is causedby substantially non-glucose reaction dependent phenomena, such asischemia that occurs within or around a glucose sensor in vivo, forexample, which results in the reaction becoming oxygen dependent. As afirst example, at high glucose levels, oxygen can become limiting to theenzymatic reaction, resulting in a non-glucose dependent downward trendin the data (best seen in FIG. 7B). As a second example, certainmovements or postures taken by the patient can cause transient downwardnoise as blood is squeezed out of the capillaries resulting in localischemia, and causing non-glucose dependent low noise. Because excessoxygen (relative to glucose) is necessary for proper sensor function,transient ischemia can result in a loss of signal gain in the sensordata. In this second example oxygen can also become transiently limiteddue to contracture of tissues around the sensor interface. This issimilar to the blanching of skin that can be observed when one putspressure on it. Under such pressure, transient ischemia can occur inboth the epidermis and subcutaneous tissue. Transient ischemia is commonand well tolerated by subcutaneous tissue.

In another example of non-glucose reaction rate-limiting phenomena, skintemperature can vary dramatically, which can result in thermally relatederosion of the signal (e.g., temperature changes between 32 and 39degrees Celsius have been measured in humans). In yet anotherembodiment, wherein the glucose sensor is placed intravenously,increased impedance can result from the sensor resting against wall ofthe blood vessel, for example, producing this non-glucose reactionrate-limiting noise due to oxygen deficiency.

Because signal artifacts are not mere system noise, but rather arecaused by specific rate-limiting mechanisms, methods used forconventional random noise filtration produce data lower (or in somecases higher) than the actual blood glucose levels due to the expansivenature of these signal artifacts. To overcome this, the preferredembodiments provide systems and methods for replacing at least some ofthe signal artifacts by estimating glucose signal values.

FIG. 8 is a flow chart that illustrates the process of detecting andreplacing signal artifacts in certain embodiments. It is noted that“signal artifacts” particularly refers to the transient non-glucoserelated artifacts that has a higher amplitude than that of system noise.Typically, signal artifacts are caused by non-glucose rate-limitingphenomenon such as described in more detail above.

At block 82, a sensor data receiving module, also referred to as thesensor data module 82, receives sensor data (e.g., a data stream),including one or more time-spaced sensor 10 data points. In someembodiments, the data stream is stored in the sensor for additionalprocessing; in some alternative embodiments, the sensor 10 periodicallytransmits the data stream to the receiver 30, which can be in wired orwireless communication with the sensor.

At block 84, a signal artifacts detection module, also referred to asthe signal artifacts detector 84, is programmed to detect transientnon-glucose related signal artifacts in the data stream that have ahigher amplitude than system noise, such as described in more detailwith reference to FIGS. 7A and 7B, above. The signal artifacts detectorcan comprise an oxygen detector, a pH detector, a temperature detector,and/or a pressure/stress detector, for example, the signal artifactsdetector 29 in FIG. 2. In some embodiments, the signal artifactsdetector at block 84 is located within the microprocessor 22 in FIG. 2and utilizes existing components of the glucose sensor 10 to detectsignal artifacts, for example by pulsed amperometric detection, counterelectrode monitoring, reference electrode monitoring, and frequencycontent monitoring, which are described elsewhere herein. In yet otherembodiments, the data stream can be sent from the sensor to the receiverwhich comprises programming in the microprocessor 42 in FIG. 4 thatperforms algorithms to detect signal artifacts, for example such asdescribed with reference to “Cone of Possibility Detection” methoddescribed in more detail below. Numerous embodiments for detectingsignal artifacts are described in more detail in the section entitled,“Signal Artifacts Detection,” all of which are encompassed by the signalartifacts detection at block 84.

At block 86, the signal artifacts replacement module, also referred toas the signal estimation module, replaces some or an entire data streamwith estimated glucose signal values using signal estimation. Numerousembodiments for performing signal estimation are described in moredetail in the section entitled “Signal Artifacts Replacement,” all ofwhich are encompassed by the signal artifacts replacement module, block86. It is noted that in some embodiments, signal estimation/replacementis initiated in response to positive detection of signal artifacts onthe data stream, and subsequently stopped in response to detection ofnegligible signal artifacts on the data stream. In some embodiments, thesystem waits a predetermined time period (e.g., between 30 seconds and30 minutes) before switching the signal estimation on or off to ensurethat a consistent detection has been ascertained. In some embodiments,however, signal estimation/replacement can continuously or continuallyrun.

Some embodiments of signal estimation can additionally includediscarding data that is considered sufficiently unreliable and/orerroneous such that the data should not be used in a signal estimationalgorithm. In these embodiments, the system can be programmed to discardoutlier data points, for example data points that are so extreme thatthey can skew the data even with the most comprehensive filtering orsignal estimation, and optionally replace those points with a projectedvalue based on historical data or present data (e.g., linear regression,recursive filtering, or the like). One example of discarding sensor dataincludes discarding sensor data that falls outside of a “Cone ofPossibility” such as described in more detail elsewhere herein. Anotherexample includes discarding sensor data when signal artifacts detectiondetects values outside of a predetermined threshold (e.g., oxygenconcentration below a set threshold, temperature above a certainthreshold, signal amplitude above a certain threshold, etc). Any of thesignal estimation/replacement algorithms described herein can then beused to project data values for those data that were discarded.

Signal Artifacts Detection

Analysis of signals from glucose sensors indicates at least two types ofnoise, which are characterized herein as 1) system noise and 2) signalartifacts, such as described in more detail above. It is noted thatsystem noise is easily smoothed using the algorithms provided herein;however, the systems and methods described herein particularly addresssignal artifacts, by replacing transient erroneous signal noise causedby rate-limiting phenomenon with estimated signal values.

In certain embodiments of signal artifacts detection, oxygen monitoringis used to detect whether transient non-glucose dependent signalartifacts due to ischemia. Low oxygen concentrations in or near theglucose sensor can account for a large part of the transient non-glucoserelated signal artifacts as defined herein on a glucose sensor signal,particularly in subcutaneously implantable glucose sensors. Accordingly,detecting oxygen concentration, and determining if ischemia exists candiscover ischemia-related signal artifacts. A variety of methods can beused to test for oxygen. For example, an oxygen-sensing electrode, orother oxygen sensor can be employed. The measurement of oxygenconcentration can be sent to a microprocessor, which determines if theoxygen concentration indicates ischemia.

In some embodiments of ischemia detection, an oxygen sensor is placedproximal to or within the glucose sensor. For example, the oxygen sensorcan be located on or near the glucose sensor such that their respectivelocal environments are shared and oxygen concentration measurement fromthe oxygen sensor represents an accurate measurement of the oxygenconcentration on or within the glucose sensor. In some alternativeembodiments of ischemia detection, an oxygen sensor is also placeddistal to the glucose sensor. For example, the oxygen sensor can belocated sufficiently far from the glucose sensor such that theirrespective local environments are not shared and oxygen measurementsfrom the proximal and distal oxygen sensors can be compared to determinethe relative difference between the respective local environments. Bycomparing oxygen concentration proximal and distal oxygen sensor, changein local (proximal) oxygen concentration can be determined from areference (distal) oxygen concentration.

Oxygen sensors are useful for a variety of purposes. For example, U.S.Pat. No. 6,512,939 to Colvin et al., which is incorporated herein byreference, discloses an oxygen sensor that measures background oxygenlevels. However, Colvin et al. rely on the oxygen sensor for the datastream of glucose measurements by subtraction of oxygen remaining afterexhaustion of glucose by an enzymatic reaction from total unreactedoxygen concentration.

In another embodiment of ischemia detection, wherein the glucose sensoris an electrochemical sensor that includes a potentiostat, pulsedamperometric detection can be employed to determine an oxygenmeasurement. Pulsed amperometric detection includes switching, cycling,or pulsing the voltage of the working electrode (or reference electrode)in an electrochemical system, for example between a positive voltage(e.g., +0.6 for detecting glucose) and a negative voltage (e.g., −0.6for detecting oxygen). U.S. Pat. No. 4,680,268 to Clark, Jr., which isincorporated by reference herein, describes pulsed amperometricdetection. In contrast to using signal replacement, Clark, Jr. addressesoxygen deficiency by supplying additional oxygen to the enzymaticreaction.

In another embodiment of ischemia detection, wherein the glucose sensoris an electrochemical sensor and contains a potentiostat, oxygendeficiency can be seen at the counter electrode when insufficient oxygenis available for reduction, which thereby affects the counter electrodein that it is unable to balance the current coming from the workingelectrode. When insufficient oxygen is available for the counterelectrode, the counter electrode can be driven in its electrochemicalsearch for electrons all the way to its most negative value, which couldbe ground or 0.0V, which causes the reference to shift, reducing thebias voltage such as described in more detail below. In other words, acommon result of ischemia will be seen as a drop off in sensor currentas a function of glucose concentration (e.g., lower sensitivity). Thishappens because the working electrode no longer oxidizes all of the H₂O₂arriving at its surface because of the reduced bias. In some extremecircumstances, an increase in glucose can produce no increase in currentor even a decrease in current.

FIG. 9 is a graph that shows a comparison of sensor current andcounter-electrode voltage in a host over time. The x-axis representstime in minutes. The first y-axis 91 represents sensor counts from theworking electrode and thus plots a raw sensor data stream 92 for theglucose sensor over a period of time. The second y-axis 93 representscounter-electrode voltage 94 in counts. The graph illustrates thecorrelation between sensor data 92 and counter-electrode voltage 94;particularly, that erroneous counter electrode function 96 where thecounter voltages drops approximately to zero substantially coincideswith transient non-glucose related signal artifacts 98. In other words,when counter-electrode voltage is at or near zero, sensor data includessignal artifacts.

In another embodiment of ischemia detection, wherein the glucose sensoris an electrochemical sensor and contains a two- or three-cellelectrochemical cell, signal artifacts are detected by monitoring thereference electrode. This “reference drift detection” embodiment takesadvantage of the fact that the reference electrode will vary or drift inorder to maintain a stable bias potential with the working electrode,such as described in more detail herein. This “drifting” generallyindicates non-glucose reaction rate-limiting noise, for example due toischemia. It is noted that the following example describes an embodimentwherein the sensor includes a working, reference, and counterelectrodes, such as described in more detail elsewhere herein; howeverthe principles of this embodiment are applicable to a two-cell (e.g.,anode and cathode) electrochemical cell as is understood in the art.

FIG. 10A is a circuit diagram of a potentiostat that controls a typicalthree-electrode system, which can be employed with a glucose sensor 10such as described with reference to FIGS. 1 and 2. The potentiostatincludes a working electrode 100, a reference electrode 102, and acounter electrode 104. The voltage applied to the working electrode is aconstant value (e.g., +1.2V) and the voltage applied to the referenceelectrode is also set at a constant value (e.g., +0.6V) such that thepotential (V_(BIAS)) applied between the working and referenceelectrodes is maintained at a constant value (e.g., +0.6V). The counterelectrode is configured to have a constant current (equal to the currentbeing measured by the working electrode), which is accomplished byvarying the voltage at the counter electrode in order to balance thecurrent going through the working electrode 100 such that current doesnot pass through the reference electrode 102. A negative feedback loop107 is constructed from an operational amplifier (OP AMP), the referenceelectrode 102, the counter electrode 104, and a reference potential, tomaintain the reference electrode at a constant voltage.

In practice, a glucose sensor of one embodiment comprises a membranethat contains glucose oxidase that catalyzes the conversion of oxygenand glucose to hydrogen peroxide and gluconate, such as described withreference to FIGS. 1 and 2. Therefore for each glucose moleculemetabolized there is a change equivalent in molecular concentration inthe co-reactant O₂ and the product H₂O₂. Consequently, one can use anelectrode (e.g., working electrode 100) to monitor theconcentration-induced current change in either the co-reactant or theproduct to determine glucose concentration.

One limitation of the electrochemistry is seen when insufficientnegative voltage is available to the counter electrode 104 to balancethe working electrode 100. This limitation can occur when insufficientoxygen is available to the counter electrode 104 for reduction, forexample. When this limitation occurs, the counter electrode can nolonger vary its voltage to maintain a balanced current with the workingelectrode and thus the negative feedback loop 107 used to maintain thereference electrode is compromised. Consequently, the referenceelectrode voltage will change or “drift,” altering the applied biaspotential (i.e., the potential applied between the working and referenceelectrodes), thereby decreasing the applied bias potential. When thischange in applied bias potential occurs, the working electrode canproduce erroneous glucose measurements due to either increased ordecreased signal strength (I_(SENSE)).

FIG. 10B a diagram referred to as Cyclic-Voltammetry (CV) curve, whereinthe x-axis represents the applied potential (V_(BIAS)) and the y-axisrepresents the signal strength of the working electrode (I_(SENSE)). Acurve 108 illustrates an expected CV curve when the potentiostat isfunctioning normally. Typically, desired bias voltage can be set (e.g.,V_(BIAS1)) and a resulting current will be sensed (e.g., I_(SENSE1)). Asthe voltage decreases (e.g., V_(BIAS2)) due to reference voltage drift,for example, a new resulting current is sensed (e.g., I_(SENSE2)).Therefore, the change in bias is an indicator of signal artifacts andcan be used in signal estimation and to replace the erroneous resultingsignals. In addition to ischemia, the local environment at the electrodesurfaces can affect the CV curve, for example, changes in pH,temperature, and other local biochemical species can significantly alterthe location of the CV curve.

FIG. 10C is a CV curve that illustrates an alternative embodiment ofsignal artifacts detection, wherein pH and/or temperature can bemonitoring using the CV curve and diagnosed to detect transientnon-glucose related signal artifacts. For example, signal artifacts canbe attributed to thermal changes and/or pH changes in some embodimentsbecause certain changes in pH and temperature affect data from a glucosesensor that relies on an enzymatic reaction to measure glucose. Signalartifacts caused by pH changes, temperature changes, changes inavailable electrode surface area, and other local biochemical speciescan be detected and signal estimation can be applied an/or optimizedsuch as described in more detail elsewhere herein. In FIG. 10C, a firstcurve 108 illustrates an expected CV curve when the potentiostat isfunctioning normally. A second curve 109 illustrates a CV curve whereinthe environment has changed as indicated by the upward shift of the CVcurve.

In some embodiments, pH and/or temperature measurements are obtainedproximal to the glucose sensor; in some embodiments, pH and/ortemperature measurements are also obtained distal to the glucose sensorand the respective measurements compared, such as described in moredetail above with reference to oxygen sensors.

In another implementation of signal artifacts detection, whereintemperature is detected, an electronic thermometer can be proximal to orwithin the glucose sensor, such that the temperature measurement isrepresentative of the temperature of the glucose sensor's localenvironment. It is noted that accurate sensor function depends ondiffusion of molecules from the blood to the interstitial fluid, andthen through the membranes of the device to the enzyme membrane.Additionally, diffusion transport of hydrogen peroxide from the enzymemembrane to the electrode occurs. Therefore, temperatures can be a ratedetermining parameter of diffusion. As temperature decreases, diffusiontransport decreases. Under certain human conditions, such as hypothermiaor fever, the variations can be considerably greater. Additionally,under normal conditions, the temperature of subcutaneous tissue is knownto vary considerably more than core tissues (e.g., core temperature).Temperature thresholds can be set to detect signal artifactsaccordingly.

In another implementation, a pH detector is used to detect signalartifacts. In glucose sensors that rely on enzymatic reactions, a pH ofthe fluid to be sensed can be within the range of about 5.5 to 7.5.Outside of this range, effects may be seen in the enzymatic reaction andtherefore data output of the glucose sensor. Accordingly, by detectingif the pH is outside of a predetermined range (e.g., 5.5 to 7.5), a pHdetector may detect transient non-glucose related signal artifacts suchas described herein. It is noted that the pH threshold can be set atranges other than provided herein without departing from the preferredembodiments.

In an alternative embodiment of signal artifacts detection, pressureand/or stress can be monitored using known techniques for example by astrain gauge placed on the sensor that detects stress/strain on thecircuit board, sensor housing, or other components. A variety ofmicroelectromechanical systems (MEMS) can be utilized to measurepressure and/or stress within the sensor.

In another alternative embodiment of signal artifacts detection, themicroprocessor in the sensor (or receiver) periodically evaluates thedata stream for high amplitude noise, which is defined by noisy datawherein the amplitude of the noise is above a predetermined threshold.For example, in the graph of FIGS. 7A and 7B, the system noise sectionssuch as 72 a and 72 b have a substantially low amplitude noisethreshold; in contrast to system noise, signal artifacts sections suchas 74 a and 74 b have signal artifacts (noise) with an amplitude that ismuch higher than that of system noise. Therefore, a threshold can be setat or above the amplitude of system noise, such that when noisy data isdetected above that amplitude, it can be considered “signal artifacts”as defined herein.

In another alternative embodiment of signal artifacts detection, amethod hereinafter referred to as the “Cone of Possibility DetectionMethod,” utilizes physiological information along with glucose signalvalues in order define a “cone” of physiologically feasible glucosesignal values within a human, such that signal artifacts are detectedwhenever the glucose signal falls outside of the cone of possibility.Particularly, physiological information depends upon the physiologicalparameters obtained from continuous studies in the literature as well asour own observations. A first physiological parameter uses a maximalsustained rate of change of glucose in humans (e.g., about 4 to 5mg/dL/min) and a maximum acceleration of that rate of change (e.g.,about 0.1 to 0.2 mg/dL/min²). A second physiological parameter uses theknowledge that rate of change of glucose is lowest at the minima, whichis the areas of greatest risk in patient treatment, and the maxima,which has the greatest long-term effect on secondary complicationsassociated with diabetes. A third physiological parameter uses the factthat the best solution for the shape of the curve at any point along thecurve over a certain time period (e.g., about 20-30 minutes) is astraight line. Additional physiological parameters can be incorporatedand are within the scope of this embodiment.

In practice, the Cone of Possibility Detection Method combines any oneor more of the above-described physiological parameters to form analgorithm that defines a cone of possible glucose levels for glucosedata captured over a predetermined time period. In one exemplaryimplementation of the Cone of Possibility Detection Method, the system(microprocessor in the sensor or receiver) calculates a maximumphysiological rate of change and determines if the data falls withinthese physiological limits; if not, signal artifacts are identified. Itis noted that the maximum rate of change can be narrowed (e.g.,decreased) in some instances. Therefore, additional physiological datacould be used to modify the limits imposed upon the Cone ofPossibilities Detection Method for sensor glucose values. For example,the maximum per minute rate change can be lower when the subject issleeping or hasn't eaten in eight hours; on the other hand, the maximumper minute rate change can be higher when the subject is exercising orhas consumed high levels of glucose, for example. In general, it hasbeen observed that rates of change are slowest near the maxima andminima of the curve, and that rates of change are highest near themidpoint between the maxima and minima. It should further be noted thatrate of change limits are derived from analysis of a range of datasignificantly higher unsustained rates of change can be observed.

In another alternative embodiment of signal artifacts detection,examination of the spectral content (e.g., frequency content) of thedata stream can yield measures useful in detecting signal artifacts. Forexample, data that has high frequency, and in some cases can becharacterized by a large negative slope, are indicative of signalartifacts and can cause sensor signal loss. Specific signal content canbe monitored using an orthogonal transform, for example a Fouriertransform, a Discrete Fourier Transform (DFT), or any other method knownin the art.

FIG. 11 is a graph of 110 a raw data stream from a glucose sensor and aspectrogram 114 that shows the frequency content of the raw data streamin one embodiment. Particularly, the graph 110 illustrates the raw datastream 112 and includes an x-axis that represents time in hours and ay-axis that represents sensor data output in counts; the spectrogram 114illustrates the frequency content 116 corresponding to the raw datastream 112 and includes an x-axis that represents time in hourscorresponding to the x-axis of the graph 110 and a y-axis thatrepresents frequency content in cycles per hour. The darkness of eachpoint represents the amplitude of that frequency at that time. Darkerpoints relate to higher amplitudes. Frequency content on the spectrogram114 was obtained using a windowed Discrete Fourier transform.

The raw data stream in the graph 110 has been adjusted by a linearmapping similar to the calibration algorithm. In this example, the bias(or intercept) has been adjusted but not the proportion (or slope). Theslope of the raw data stream would typically be determined by somecalibration, but since the calibration has not occurred in this example,the gray levels in the spectrogram 114 indicate relative values. Thelower values of the graph 110 are white. They are colored as white belowa specific value, highlighting only the most intense areas of the graph.

By monitoring the frequency content 116, high frequency cycles 118 canbe observed. The high frequency cycles 118 correspond to signalartifacts 119 such as described herein. Thus, signal artifacts can bedetected on a data stream by monitoring frequency content and setting athreshold (e.g., 30 cycles per hour). The set threshold can varydepending on the signal source.

In another alternative embodiment of signal artifacts detection,examination of the signal information content can yield measures usefulin detecting signal artifacts. Time series analysis can be used tomeasure entropy, approximate entropy, variance, and/or percent change ofthe information content over consecutive windows (e.g., 30 and 60 minutewindows of data) of the raw data stream. In one exemplary embodiment,the variance of the raw data signal is measured over 15 minute and 45minute windows, and signal artifacts are detected when the variance ofthe data within the 15-minute window exceeds the variance of the datawithin the 45-minute window.

One or a plurality of the above signal artifacts detection models can beused alone or in combination to detect signal artifacts such asdescribed herein. Accordingly, the data stream associated with thesignal artifacts can be discarded, replaced, or otherwise processed inorder to reduce or eliminate these signal artifacts and thereby improvethe value of the glucose measurements that can be provided to a user.

Signal Artifacts Replacement

Signal Artifacts Replacement, such as described above, can use systemsand methods that reduce or replace these signal artifacts that can becharacterized by transience, high frequency, high amplitude, and/orsubstantially non-linear noise. Accordingly, a variety of filters,algorithms, and other data processing are provided that address thedetected signal artifacts by replacing the data stream, or portion ofthe data stream, with estimated glucose signal values. It is noted that“signal estimation” as used herein, is a broad term, which includesfiltering, data smoothing, augmenting, projecting, and/or otheralgorithmic methods that estimate glucose signal values based on presentand historical data.

It is noted that a glucose sensor can contain a microprocessor or thelike that processes periodically received raw sensor data (e.g., every30 seconds). Although a data point can be available constantly, forexample by use of an electrical integration system in a chemo-electricsensor, relatively frequent (e.g., every 30 seconds), or less frequentdata point (e.g., every 5 minutes), can be more than sufficient forpatient use. It is noted that accordingly Nyquist Theory, a data pointis required about every 10 minutes to accurately describe physiologicalchange in glucose in humans. This represents the lowest useful frequencyof sampling. However, it should be recognized that it is desirable tosample more frequently than the Nyquist minimum, to provide forsufficient data in the event that one or more data points are lost, forexample. Additionally, more frequently sampled data (e.g., 30-second)can be used to smooth the less frequent data (e.g., 5-minute) that aretransmitted. It is noted that in this example, during the course of a5-minute period, 10 determinations are made at 30-second intervals.

In some embodiments of Signal Artifacts Replacement, signal estimationcan be implemented in the sensor and transmitted to a receiver foradditional processing. In some embodiments of Signal ArtifactsReplacement, raw data can be sent from the sensor to a receiver forsignal estimation and additional processing therein. In some embodimentsof Signal Artifacts Replacement, signal estimation is performedinitially in the sensor, with additional signal estimation in thereceiver.

In some embodiments of Signal Artifacts Replacement, wherein the sensoris an implantable glucose sensor, signal estimation can be performed inthe sensor to ensure a continuous stream of data. In alternativeembodiments, data can be transmitted from the sensor to the receiver,and the estimation performed at the receiver; It is noted however thatthere can be a risk of transmit-loss in the radio transmission from thesensor to the receiver when the transmission is wireless. For example,in embodiments wherein a sensor is implemented in vivo, the raw sensorsignal can be more consistent within the sensor (in vivo) than the rawsignal transmitted to a source (e.g., receiver) outside the body (e.g.,if a patient were to take the receiver off to shower, communicationbetween the sensor and receiver can be lost and data smoothing in thereceiver would halt accordingly). Consequently, It is noted that amultiple point data loss in the filter can take for example, about 25 toabout 40 minutes for the data to recover to near where it would havebeen had there been no data loss.

In some embodiments of Signal Artifacts Replacement, signal estimationis initiated only after signal artifacts are positively detected, andstopped once signal artifacts are negligibly detected. In somealternative embodiments signal estimation is initiated after signalartifacts are positively detected and then stopped after a predeterminedtime period. In some alternative embodiments, signal estimation can becontinuously or continually performed. In some alternative embodiments,one or more forms of signal estimation can be accomplished based on theseverity of the signal artifacts, such as will be described withreference the section entitled, “Selective Application of SignalArtifacts Replacement.”

In some embodiments of Signal Artifacts Replacement, the microprocessorperforms a linear regression. In one such implementation, themicroprocessor performs a linear regression analysis of the n (e.g., 10)most recent sampled sensor values to smooth out the noise. A linearregression averages over a number of points in the time course and thusreduces the influence of wide excursions of any point from theregression line. Linear regression defines a slope and intercept, whichis used to generate a “Projected Glucose Value,” which can be used toreplace sensor data. This regression can be continually performed on thedata stream or continually performed only during the transient signalartifacts. In some alternative embodiments, signal estimation caninclude non-linear regression.

In another embodiment of Signal Artifacts Replacement, themicroprocessor performs a trimmed regression, which is a linearregression of a trimmed mean (e.g., after rejecting wide excursions ofany point from the regression line). In this embodiment, after thesensor records glucose measurements at a predetermined sampling rate(e.g., every 30 seconds), the sensor calculates a trimmed mean (e.g.,removes highest and lowest measurements from a data set and thenregresses the remaining measurements to estimate the glucose value.

FIG. 12 is a graph that illustrates a raw data stream from a glucosesensor and a trimmed regression that can be used to replace some of orthe entire data stream. The x-axis represents time in minutes; they-axis represents sensor data output in counts. A raw data signal 120,which is illustrated as a dotted line, shows a data stream wherein somesystem noise can be detected, however signal artifacts 122 can beparticularly seen in a portion thereof (and can be detected by methodssuch as described above). The trimmed regression line 124, which isillustrated as a solid line, is the data stream after signal estimationusing a trimmed linear regression algorithm, such as described above,and appears at least somewhat “smoothed” on the graph. In thisparticular example, the trimmed regression uses the most recent 60points (30 minutes) and trims out the highest and lowest values, thenuses the leftover 58 points to project the next point. It is noted thatthe trimmed regression 124 provides a good estimate throughout themajority data stream, however is only somewhat effective in smoothingthe data in during signal artifacts 122. To provide an optimizedestimate of the glucose data values, the trimmed regression can beoptimized by changing the parameters of the algorithm, for example bytrimming more or less raw glucose data from the top and/or bottom of thesignal artifacts 122 prior to regression. Additionally It is noted thattrimmed regression, because of its inherent properties, can beparticularly suited for noise of a certain amplitude and/orcharacteristic. In one embodiment, for example trimmed regression can beselectively applied based on the severity of the signal artifacts, whichis described in more detail below with reference to FIGS. 15 to 17.

In another embodiment of Signal Artifacts Replacement, themicroprocessor runs a non-recursive filter, such as a finite impulseresponse (FIR) filter. A FIR filter is a digital signal filter, in whichevery sample of output is the weighted sum of past and current samplesof input, using only some finite number of past samples.

FIG. 13 a graph that illustrates a raw data stream from a glucose sensorand an FIR-estimated signal that can be used to replace some of or theentire data stream. The x-axis represents time in minutes; the y-axisrepresents sensor data output in counts. A raw data signal 130, which isillustrated as a dotted line, shows a data stream wherein some systemnoise can be detected, however signal artifacts 132 can be particularlyseen in a portion thereof (and can be detected by methods such asdescribed above). The FIR-estimated signal 134, which is illustrated asa solid line, is the data stream after signal estimation using a FIRfilter, such as described above, and appears at least somewhat“smoothed” on the graph. It is noted that the FIR-estimated signalprovides a good estimate throughout the majority of the data stream,however like trimmed regression it is only somewhat effective insmoothing the data during signal artifacts 132. To provide an optimizedestimate of the glucose data values, the FIR filter can be optimized bychanging the parameters of the algorithm, for example the tuning of thefilter, particularly the frequencies of the pass band and the stop band.Additionally It is noted that the FIR filter, because of its inherentproperties, can be particularly suited for noise of a certain amplitudeand/or characteristic. In one embodiment, for example the FIR filter canbe selectively applied based on the severity of the signal artifacts,which is described in more detail below with reference to FIGS. 15 to17. It is noted that the FIR-estimated signal induces a time lag on thedata stream, which can be increased or decreased in order to optimizethe filtering or to minimize the time lag, for example.

In another embodiment of Signal Artifacts Replacement, themicroprocessor runs a recursive filter, such as an infinite impulseresponse (IIR) filter. An IIR filter is a type of digital signal filter,in which every sample of output is the weighted sum of past and currentsamples of input. In one exemplary implementation of an IIR filter, theoutput is computed using 6 additions/subtractions and 7 multiplicationsas shown in the following equation:

${y(n)} = \frac{\begin{matrix}{{a_{0}*{x(n)}} + {a_{1}*{x\left( {n - 1} \right)}} + {a_{2}*{x\left( {n - 2} \right)}} + {a_{3}*{x\left( {n - 3} \right)}} -} \\{{b_{1}*{y\left( {n - 1} \right)}} - {b_{2}*{y\left( {n - 2} \right)}} - {b_{3}*{y\left( {n - 3} \right)}}}\end{matrix}}{b_{0}}$

This polynomial equation includes coefficients that are dependent onsample rate and frequency behavior of the filter. Frequency behaviorpasses low frequencies up to cycle lengths of 40 minutes, and is basedon a 30 second sample rate. In alternative implementations, the samplerate and cycle lengths can be more or less. See Lynn “Recursive DigitalFilters for Biological Signals” Med. & Biol. Engineering, Vol. 9, pp.37-43, which is incorporated herein by reference in its entirety.

FIG. 14 is a graph that illustrates a raw data stream from a glucosesensor and an IIR-estimated signal that can be used to replace some ofor the entire data stream. The x-axis represents time in minutes; they-axis represents sensor data output in counts. A raw data signal 140,which is illustrated as a dotted line, shows a data stream wherein somesystem noise can be detected, however signal artifacts 142 can beparticularly seen in a portion thereof (and can be detected by methodssuch as described above). The IIR-estimated signal 144, which isillustrated as a solid line, represents the data stream after signalestimation using an IIR filter, such as described above, and appears atleast somewhat “smoothed” on the graph. It is noted that theIIR-estimated signal induces a time lag on the data stream, however itappears to be a particularly good estimate of glucose data values duringsignal artifacts 142, as compared to the FIR filter (FIG. 13), forexample.

To optimize the estimation of the glucose data values, the parameters ofthe IIR filter can be optimized, for example by increasing or decreasingthe cycle lengths (e.g., 10 minutes, 20 minute, 40 minutes, 60 minutes)that are used in the algorithm. Although an increased cycle length canincrease the time lag induced by the IIR filter, an increased cyclelength can also better estimate glucose data values during severe signalartifacts. In other words, It is noted that the IIR filter, because ofits inherent properties, can be particularly suited for noise of acertain amplitude and/or characteristic. In one exemplary embodiment,the IIR filter can be continually applied, however the parameters suchas described above can be selectively altered based on the severity ofthe signal artifacts; in another exemplary embodiment, the IIR filtercan be applied only after positive detection of signal artifacts.Selective application of the IIR filter based on the severity of thesignal artifacts is described in more detail below with reference toFIGS. 15 to 17.

It is noted that a comparison of linear regression, an FIR filter, andan IIR filter can be advantageous for optimizing their usage in thepreferred embodiments. That is, an understanding the designconsiderations for each algorithm can lead to optimized selection andimplementation of the algorithm, as described in the section entitled,“Selective Application of Signal Replacement Algorithms” herein. Duringsystem noise, as defined herein, all of the above algorithms can besuccessfully implemented during system noise with relative ease. Duringsignal artifacts, however, computational efficiency is greater with anIIR-filter as compared with linear regression and FIR-filter.Additionally, although the time lag associated with an IIR filter can besubstantially greater than that of the linear regression or FIR-filter,this can be advantageous during severe signal artifacts in order toassign greater weight toward the previous, less noisy data in signalestimation.

In another embodiment of Signal Artifacts Replacement, themicroprocessor runs a maximum-average (max-average) filtering algorithm.The max-average algorithm smoothes data based on the discovery that thesubstantial majority of signal artifacts observed after implantation ofglucose sensors in humans, for example, is not distributed evenly aboveand below the actual blood glucose levels. It has been observed thatmany data sets are actually characterized by extended periods in whichthe noise appears to trend downwardly from maximum values withoccasional high spikes such as described in more detail above withreference to FIG. 7B, section 74 b, which is likely in response tolimitations in the system that do not allow the glucose to fully reactat the enzyme layer and/or proper reduction of H₂O₂ at the counterelectrode, for example. To overcome these downward trending signalartifacts, the max-average calculation tracks with the highest sensorvalues, and discards the bulk of the lower values. Additionally, themax-average method is designed to reduce the contamination of the datawith unphysiologically high data from the high spikes.

The max-average calculation smoothes data at a sampling interval (e.g.,every 30 seconds) for transmission to the receiver at a less frequenttransmission interval (e.g., every 5 minutes) to minimize the effects oflow non-physiological data. First, the microprocessor finds and stores amaximum sensor counts value in a first set of sampled data points (e.g.,5 consecutive, accepted, thirty-second data points). A frame shift timewindow finds a maximum sensor counts value for each set of sampled data(e.g., each 5-point cycle length) and stores each maximum value. Themicroprocessor then computes a rolling average (e.g., 5-point average)of these maxima for each sampling interval (e.g., every 30 seconds) andstores these data. Periodically (e.g., every 10^(th) interval), thesensor outputs to the receiver the current maximum of the rollingaverage (e.g., over the last 10 thirty-second intervals as a smoothedvalue for that time period (e.g., 5 minutes)). In one exampleimplementation, a 10-point window can be used, and at the 10^(th)interval, the microprocessor computes the average of the most recent 5or 10 average maxima as the smoothed value for a 5 minute time period.

In some embodiments of the max-average algorithm, an acceptance filtercan also be applied to new sensor data to minimize effects of highnon-physiological data. In the acceptance filter, each sampled datapoint (e.g., every 30-seconds) is tested for acceptance into the maximumaverage calculation. Each new point is compared against the mostrepresentative estimate of the sensor curve at the previous samplinginterface (e.g., 30-second time point), or at a projection to a currentestimated value. To reject high data, the current data point is comparedto the most recent value of the average maximum values over a timeperiod (e.g., 5 sampled data points over a 2.5 minute period). If theratio of current value to the comparison value is greater than a certainthreshold (e.g., about 1.02), then the current data point is replacedwith a previously accepted value (e.g., 30-second value). If the ratioof current value to the comparison value is in at or within a certainrange (e.g., about 1.02 to 0.90), then the current data point isaccepted. If the ratio of current value to the comparison value is lessthan a certain threshold (e.g., about 0.90), then the current data pointis replaced with a previously accepted value. The acceptance filter stepand max-average calculation are continuously run throughout the data set(e.g., fixed 5-minute windows) on a rolling window basis (e.g., every 30seconds).

In some implementations of the acceptance filter, the comparison valuefor acceptance could also be the most recent maximum of 5 acceptedsensor points (more sensitive) or the most recent average over 10averages of 5 maximum values (least sensitive), for example. In someexemplary implementations of the acceptance filter, the projected valuefor the current time point can be based on regression of the last 4accepted 30-second values and/or the last 2 to 4 (5 to 15 min) of the5-minute smoothed values, for example. In some exemplary implementationsof the acceptance filter, the percentage comparisons of +2% and −10% ofcounts value would be replaced by percentage comparisons based on themost recent 24 hour range of counts values; this method would provideimproved sensor specificity as compared to a method based on totalcounts.

In another embodiment of Signal Artifacts Replacement, themicroprocessor runs a “Cone of Possibility Replacement Method.” It isnoted that this method can be performed in the sensor and/or in thereceiver. The Cone of Possibility Detection Method utilizesphysiological information along with glucose signal values in orderdefine a “cone” of physiologically feasible glucose signal values withina human. Particularly, physiological information depends upon thephysiological parameters obtained from continuous studies in theliterature as well as our own observations. A first physiologicalparameter uses a maximal sustained rate of change of glucose in humans(e.g., about 4 to 5 mg/dl/min) and a maximum sustained acceleration ofthat rate of change (e.g., about 0.1 to 0.2 mg/min/min). A secondphysiological parameter uses the knowledge that rate of change ofglucose is lowest at the maxima and minima, which are the areas ofgreatest risk in patient treatment, such as described with reference toCone of Possibility Detection, above. A third physiological parameteruses the fact that the best solution for the shape of the curve at anypoint along the curve over a certain time period (e.g., about 20-25minutes) is a straight line. It is noted that the maximum rate of changecan be narrowed in some instances. Therefore, additional physiologicaldata can be used to modify the limits imposed upon the Cone ofPossibility Replacement Method for sensor glucose values. For example,the maximum per minute rate change can be lower when the subject islying down or sleeping; on the other hand, the maximum per minute ratechange can be higher when the subject is exercising, for example.

The Cone of Possibility Replacement Method utilizes physiologicalinformation along with blood glucose data in order to improve theestimation of blood glucose values within a human in an embodiment ofSignal Artifacts Replacement. The Cone of Possibility Replacement Methodcan be performed on raw data in the sensor, on raw data in the receiver,or on smoothed data (e.g., data that has been replaced/estimated in thesensor or receiver by one of the methods described above) in thereceiver.

In a first implementation of the Cone of Possibility Replacement Method,a centerline of the cone can be projected from a number of previous,optionally smoothed, incremental data points (e.g., previous four,5-minute data points). Each predicted cone centerline point (e.g., 5minute point) increases by the slope (S) (e.g., for the regression incounts/minute) multiplied by the data point increment (e.g., 5 minutes).Counts/mg/dL is estimated from glucose and sensor range calculation overthe data set.

In this first implementation of the Cone of Possibility ReplacementMethod, positive and negative cone limits are simple linear functions.Periodically (e.g., every 5 minutes), each sensor data point (optionallysmoothed) is compared to the cone limits projected from the last fourpoints. If the sensor value observed is within the cone limits, thesensor value is retained and used to generate the cone for the next datapoint increment (e.g., 5-minute point). If the sensor value observedfalls outside the high or low cone limit, the value is replaced by thecone limit value, and that value is used to project the next data pointincrement (e.g., 5 minute point, high point, or low point). For example,if the difference between two adjacent 5-minute points exceeds 20 mg/dL,then cone limits are capped at 20 mg/dL increments per 5 minutes, withthe positive limit of the cone being generated by the addition of0.5*A*t² to mid cone value, where A is 0.1 mg/dL/min/min and t is 5minute increments (A is converted to counts/min/min for thecalculation), and the negative limit of the cone being generated by theaddition of −0.5*A*t² to mid cone value. This implementation provides ahigh degree of accuracy and is minimally sensitive to non-physiologicalrapid changes.

The following table illustrates one example implementation of the Coneof Possibility Replacement Method, wherein the maximum sustained valueobserved for S is about +/−4 mg/dL/min and the maximum value observedfor A is about +/−0.1 mg/dL/min²:

Mid line Time (mg/dL) Positive Cone Limit Negative Cone Limit 0 100 100100 5 100 + 5 * S 100 + 5 * S + 12.5 * A 100 + 5 * S − 12.5A 10 100 +10 * S 100 + 10 * S + 50 * A 100 + 10 * S − 50 * A 15 100 + 15 * S 100 +15 * S + 112.5 * A 100 + 15 * S − 112.5 * A 20 100 + 20 * S 100 + 20 *S + 200 * A 100 + 20 * S − 200 * A 25 100 + 25 * S 100 + 25 * S +312.5 * A 100 + 25 * S − 312.5 * A

It is noted that the cone widens for each 5-minute increment for which asensor value fails to fall inside the cone up to 30 minutes, such as canbe seen in the table above. At 30 minutes, a cone has likely widenedenough to capture an observed sensor value, which is used, and the conecollapses back to a 5-minute increment width. If no sensor values arecaptured within 30 minutes, the cone generation routine starts overusing the next four observed points. In some implementations specialrules can be applied, for example in a case where the change in countsin one 5-minute interval exceeds an estimated 30-mg/dL amount. In thiscase, the next acceptable point can be more than 20 to 30 minutes later.It is noted that an implementation of this algorithm includes utilizingthe cone of possibility to predict glucose levels and alert patients topresent or upcoming dangerous blood glucose levels.

In another alternative embodiment of cone widening, the cone can widenin set multiples (e.g., 20 mg/dL) of equivalent amounts for eachadditional time interval (e.g., 5 minutes), which rapidly widens thecone to accept data.

It is noted that the numerical parameters represent only one exampleimplementation of the Cone of Possibility Replacement Method. Theconcepts can be applied to any numerical parameters as desired forvarious glucose sensor applications.

In another implementation of the Cone of Possibility Replacement Method,sensor calibration data is optimized using the Clarke Error Grid, theConsensus Grid, or an alternative error assessment that assigns risklevels based on the accuracy of matched data pairs. In an example usingthe Clarke Error Grid, because the 10 regions of the Clarke Error Gridare not all symmetric around the Y=X perfect regression, fits to thegrid can be improved by using a multi-line regression to the data.

Accordingly the pivot point method for the counts vs. glucose regressionfit can be used to optimize sensor calibration data to the Clarke ErrorGrid, Consensus Grid, or other clinical acceptability standard. First,the glucose range is divided according to meter values (e.g., at 200mg/dL). Two linear fitting lines are used, which cross at the pivotpoint. The coordinates of the pivot point in counts and glucose value,plus the slope and intercept of the two lines are variable parameters.Some of pivot point coordinates (e.g., 4 out of 6) and slope orintercept of each line can be reset with each iteration, while thechosen coordinates define the remainder. The data are then re-plotted onthe Clarke Error Grid, and changes in point placement and percentages ineach region of the grid are evaluated. To optimize the fit of a data setto a Clark Error Grid, the regression of counts vs. reference glucosecan be adjusted such that the maximum number of points are in the A+Bzones without reducing the A+B percentage, and the number of points areoptimized such that the highest percentage are in the A zone and lowestpercentage are in the D, E and C zones. In general, the points should bedistributed as evenly as possible around the Y=X line. In someembodiments, three distinct lines optimized for clinical acceptabilitycan represent the regression line. In some embodiments, an additionaluseful criterion can be used to compute the root mean squared percentagebias for the data set. Better fits are characterized by reduction in thetotal root mean squared percentage bias. In an alternativeimplementation of the pivot point methods, a predetermined pivot (e.g.,10 degree) of the regression line can be applied when the estimatedblood is above or below a set threshold (e.g., 150 mg/dL), wherein thepivot and threshold are determined from a retrospective analysis of theperformance of a conversion function and its performance at a range ofglucose concentrations.

In another embodiment of Signal Artifacts Replacement, reference changesin electrode potential can be used to estimate glucose sensor dataduring positive detection of signal artifacts from an electrochemicalglucose sensor, the method hereinafter referred to as reference driftreplacement. In this embodiment, the electrochemical glucose sensorcomprises working, counter, and reference electrodes, such as describedwith reference to FIGS. 1, 2 and 10 above. This method exploits thefunction of the reference electrode as it drifts to compensate forcounter electrode limitations during oxygen deficits, pH changes, and/ortemperature changes such as described in more detail above withreference to FIGS. 10A, 10B, and 10C.

Such as described with in more detail with reference to FIG. 10A apotentiostat is generally designed so that a regulated potentialdifference between the reference electrode 102 and working electrode 100is maintained as a constant. The potentiostat allows the counterelectrode voltage to float within a certain voltage range (e.g., frombetween close to the +1.2V observed for the working electrode to as lowas battery ground or 0.0V). The counter electrode voltage measurementwill reside within this voltage range dependent on the magnitude andsign of current being measured at the working electrode and theelectroactive species type and concentration available in theelectrolyte adjacent to the counter electrode 104. This species will beelectrochemically recruited (e.g., reduced/accepting electrons) to equalthe current of opposite sign (e.g., oxidized/donating electrons)occurring at the working electrode 100. It has been discovered that thereduction of dissolved oxygen or hydrogen peroxide from oxygen convertedin the enzyme layer are the primary species reacting at the counterelectrode to provide this electronic current balance in this embodiment.If there are inadequate reducible species (e.g., oxygen) available forthe counter electrode, or if other non-glucose reaction rate limitingphenomena occur (e.g., temperature or pH), the counter electrode can bedriven in its electrochemical search for electrons all the way to groundor 0.0V. However, regardless of the voltage in the counter electrode,the working and counter electrode currents must still maintainsubstantially equivalent currents. Therefore, the reference electrode102 will drift upward creating new oxidizing and reducing potentialsthat maintain equal currents at the working and counter electrodes.

Because of the function of the reference electrode 102, including thedrift that occurs during periods of signal artifacts (e.g., ischemia),the reference electrode can be monitored to determine the severity ofthe signal artifacts on the data stream. Particularly, a substantiallydirect relationship between the reference electrode drift and signalartifacts has been discovered. Using the information contained withinthe CV curve (FIGS. 10B and/or 10C), the measured glucose signal(I_(SENSE)) can be automatically scaled accordingly to replace theseundesired transient effects on the data stream. It is noted that thecircuit described with reference to FIG. 10A can be used to determinethe CV curve on a regularly scheduled basis or as needed. To this end,the desired reference voltage and applied potential are made variable,and the reference voltage can be changed at a defined rate whilemeasuring the signal strength from the working electrode, which allowsfor generation of a CV curve while a sensor is in vivo.

In alternative implementations of the reference drift replacementmethod, a variety of algorithms can therefore be implemented thatreplaces the signal artifacts based on the changes measured in thereference electrode. Linear algorithms, and the like, are suitable forinterpreting the direct relationship between reference electrode driftand the non-glucose rate limiting signal noise such that appropriateconversion to signal noise compensation can be derived.

In other embodiments of Signal Artifacts Replacement, predictionalgorithms, also referred to as projection algorithms, can be used toreplace glucose data signals for data which does not exist because 1) ithas been discarded, 2) it is missing due to signal transmission errorsor the like, or 3) it represents a time period (e.g., future) for whicha data stream has not yet been obtained based on historic and/or presentdata. Prediction/projection algorithms include any of the abovedescribed Signal Artifacts Replacement algorithms, and differ only inthe fact that they are implemented to replace time points/periods duringwhich no data is available (e.g., for the above-described reasons),rather than including that existing data, within the algorithmiccomputation.

In some embodiments, signal replacement/estimation algorithms are usedto predict where the glucose signal should be, and if the actual datastream varies beyond a certain threshold of that projected value, thensignal artifacts are detected. In alternative embodiments, other dataprocessing can be applied alone, or in combination with theabove-described methods, to replace data signals during system noiseand/or signal artifacts.

Selective Application of Signal Replacement Algorithms

FIG. 15 is a flow chart that illustrates a process of selectivelyapplying signal estimation in embodiments.

At block 152, a sensor data receiving module, also referred to as thesensor data module, receives sensor data (e.g., a data stream),including one or more time-spaced sensor data points, such as describedin more detail with reference to block 82 in FIG. 8.

At block 154, a signal artifacts detection module, also referred to asthe signal artifacts detector 154, is programmed to detect transientnon-glucose related signal artifacts in the data stream that have ahigher amplitude than system noise, such as described in more detailwith reference to block 84 in FIG. 8. However, the signal artifactsdetector of this embodiment can additionally detect a severity of signalartifacts. In some embodiments, the signal artifacts detector has one ormore predetermined thresholds for the severity of the signal artifacts(e.g., low, medium, and high). In some embodiments, the signal artifactsdetector numerically represents the severity of signal artifacts basedon a calculation for example, which representation can be used to applyto the signal estimation algorithm factors, such as described in moredetail with reference to block 156.

In one exemplary embodiment, the signal artifacts detection moduleevaluates the amplitude and/or frequency of the transient non-glucoserelated signal artifacts, which amplitude and/or frequency can be usedto define the severity in terms of a threshold (e.g., high or low) or anumeric representation (e.g., a value from 1 to 10). In anotherexemplary embodiment, the signal artifacts detection module evaluates aduration of the transient non-glucose related signal artifacts, suchthat as the duration increases, a severity can be defined in terms of athreshold (e.g., short or long) or a numeric representation (e.g., 10,20, 30, 40, 50, or 60 minutes). In another exemplary embodiment, thesignal artifacts detection module evaluates the frequency content from aFourier Transform and defines severity in terms of a threshold (e.g.,above or below 30 cycles per hour) or a numeric representation (e.g., 50cycles per hour). All of the signal artifacts detection methodsdescribed herein can be implemented to include determining a severity ofthe signal artifacts, threshold, and/or numerical representations.

At block 156, the signal artifacts replacement module, also referred toas the signal estimation module, selectively applies one of a pluralityof signal estimation algorithm factors in response to the severity ofsaid signal artifacts.

In one embodiment, signal artifacts replacement is normally turned off,except during detected signal artifacts. In another embodiment, a firstsignal estimation algorithm (e.g., linear regression, FIR filter etc.)is turned on all the time, and a second signal estimation algorithmoptimized for signal artifacts (e.g., IIR filter, Cone of PossibilityReplacement Method, etc.) is turned on only during positive detection ofsignal artifacts.

In another embodiment, the signal replacement module comprisesprogramming to selectively switch on and off a plurality of distinctsignal estimation algorithms based on the severity of the detectedsignal artifacts. For example, the severity of the signal artifacts canbe defined as high and low. In such an example, a first filter (e.g.,trimmed regression, linear regression, FIR, Reference Electrode Method,etc.) can be applied during low signal artifacts and a second filter(e.g., IIR, Cone of Possibility Method, etc.) can be applied during highsignal artifacts. It is noted that all of the above signal replacementalgorithms can be selectively applied in this manner based on theseverity of the detected signal artifacts.

FIG. 16 is a graph that illustrates a embodiment wherein the signalreplacement module comprises programming to selectively switch on andoff a signal artifacts replacement algorithm responsive to detection ofsignal artifacts. The x-axis represents time in minutes; the firsty-axis 160 represents sensor data output in counts. A raw data signal161, which is illustrated as a dotted line, shows a data stream whereinsome system noise can be detected; however signal artifacts 162 can beparticularly seen in a portion thereof. The second y-axis 164 representscounter-electrode voltage in counts; counter electrode voltage data 165is illustrated as a solid line. It is noted that a counter voltage dropto approximately zero in this example, which is one of numerous methodsprovided for detecting signal artifacts, detects signal artifacts 162.Accordingly, when the system detects the signal artifacts 162, anIIR-filter is selectively switched on in order to replace the signalartifact with an IIR-estimated glucose signal 166, which is illustratedas a heavy solid line. The IIR filter is switched off upon detection ofnegligible signal artifacts (e.g., counter electrode voltage increasingfrom about zero in this embodiment).

FIG. 17 is a graph that illustrates a embodiment wherein the signalartifacts replacement module comprises programming to selectively applydifferent signal artifacts replacement algorithms responsive todetection of signal artifacts. The x-axis represents time in minutes;the first y-axis 170 represents sensor data output in counts. A raw datasignal 171, which is illustrated as a dotted line, shows a data streamwherein some system noise can be detected; however signal artifacts 172can be particularly seen in a portion thereof. The second y-axis 174represents counter-electrode voltage in counts; counter electrodevoltage data 175 is illustrated as a solid line. It is noted that acounter voltage drop to approximately zero in this example, which is oneof numerous methods provided for detecting signal artifacts, detectssignal artifacts 172.

In this embodiment, an FIR filter is applied to the data stream duringdetection of negligible or no signal artifacts (e.g., during no noise tosystem noise in the data stream). Accordingly, normal signal noise(e.g., system noise) can be filtered to replace the data stream with anFIR-filtered data signal 176, which is illustrated by a slightly heavysolid line. However, upon positive detection of signal artifacts (e.g.,detected by approximately zero counter electrode voltage in thisembodiment), the FIR filter is switched off and an IIR-filter isswitched on in order to replace the signal artifacts with anIIR-filtered glucose signal 178, which is illustrated as a heavy solidline. The IIR filter is subsequently switched off and the FIR filter isswitched back on upon detection of negligible signal artifacts (e.g.,counter electrode voltage increasing from about zero in thisembodiment).

In another embodiment, the signal replacement module comprisesprogramming to selectively apply different parameters to a single signalartifacts replacement algorithm (e.g., IIR, Cone of PossibilityReplacement Method, etc.). As an example, the parameters of an algorithmcan be switched according to signal artifacts detection; in such anexample, an IIR filter with a 30-minute cycle length can be used duringtimes of no noise or system noise and a 60-minute cycle length can beused during signal artifacts. As another example, the severity of thesignal artifacts can be defined as short and long; in such an example,an IIR filter with a 30-minute cycle length can be used during the shortsignal artifacts and a 60-minute cycle length can be used during longsignal artifacts. As yet another example, the severity of the signalartifacts can be defined by a numerical representation; in such anexample, the numerical representation can be used to calculate theparameters of the signal replacement algorithm (e.g., IIR, Cone ofPossibility Replacement Method, and Reference Drift Method).

The above description provides several methods and materials of theinvention. This invention is susceptible to modifications in the methodsand materials, as well as alterations in the fabrication methods andequipment. Such modifications will become apparent to those skilled inthe art from a consideration of this application or practice of theinvention provided herein. Consequently, it is not intended that thisinvention be limited to the specific embodiments provided herein, butthat it cover all modifications and alternatives coming within the truescope and spirit of the invention as embodied in the attached claims.All patents, applications, and other references cited herein are herebyincorporated by reference in their entirety.

What is claimed is:
 1. A method for processing data from a continuousglucose sensor, the method comprising: monitoring a data streamgenerated by a working electrode of a continuous glucose sensor, thedata stream comprising a glucose sensor data point; monitoring an outputof a counter electrode of the continuous glucose sensor; detecting asignal artifact associated with the glucose sensor data point;classifying the signal artifact based on the monitoring of the output ofthe counter electrode; selectively applying one or more of a pluralityof signal estimation algorithms based on the classification to generatean estimated sensor data point; and replacing the sensor data point withthe estimated sensor data point.
 2. The method of claim 1, wherein thedata stream comprises a raw data stream.
 3. The method of claim 1,wherein the data stream comprises a calibrated data stream of glucoseconcentration values.
 4. The method of claim 1, wherein the signalartifact is a transient non-glucose related signal artifact.
 5. Themethod of claim 1, further comprising displaying or transmitting theestimated data point.
 6. The method of claim 1, wherein the detectingcomprises identifying a drop in the output of the counter electrode andassociating the output drop with the glucose sensor data point.
 7. Themethod of claim 1, wherein electronic circuitry is used to perform oneor more of the monitoring of the data stream, the monitoring of theoutput, the detecting, the classifying, the selectively applying and thereplacing.
 8. The method of claim 1, wherein the continuous glucosesensor is an implantable sensor comprising the working electrode, thecounter electrode and a reference electrode.
 9. The method of claim 1,wherein the detecting comprises comparing the data stream with theoutput of the counter electrode.
 10. The method of claim 1, wherein theoutput of the counter electrode is measured in units of counts.
 11. Themethod of claim 1, wherein the classifying comprises classifying aseverity of the signal artifact.
 12. A system for processing data from acontinuous glucose sensor, the system comprising: a continuous glucosesensor comprising a working electrode configured to generate a datastream and a counter electrode configured to generate a counterelectrode output; and an artifact replacement module configured to:monitor the counter electrode output; detect a signal artifactassociated with a portion of the data stream; classify the signalartifact based on the monitoring of the counter electrode output;selectively apply one or more of a plurality of signal estimationalgorithms based on the classification to generate an estimated datastream portion; and replace the portion of the data stream with theestimated data stream portion.
 13. The system of claim 12, wherein thedata stream comprises a raw data stream.
 14. The system of claim 12,wherein the data stream comprises a calibrated data stream of glucoseconcentration values.
 15. The system of claim 12, wherein the signalartifact is a transient non-glucose related signal artifact.
 16. Thesystem of claim 12, further comprising an output module configured todisplay or transmit the information representative of the estimated datastream portion.
 17. The system of claim 12, wherein the artifactreplacement module is configured to detect by identifying a drop in thecounter electrode output and associating the output drop with theportion of the data stream.
 18. The system of claim 12, wherein thecontinuous glucose sensor is an implantable sensor and further comprisesa reference electrode.
 19. The system of claim 12, wherein the artifactreplacement module is configured to detect by comparing the data streamwith the counter electrode output.
 20. The system of claim 12, whereinthe counter electrode output is measured in units of counts.